System

Abstract

A system is provided.SOLUTION: A terminal unit configured to capture a photograph of food or drink by a user; a transmission unit configured to transmit the captured photograph to a server; A system includes image analysis means for specifying a type and an amount of food and drink, nutrition data acquisition means for acquiring nutrition component data of the specified food and drink with reference to a food database, health care data acquisition means for acquiring health care data of a user registered in advance, nutrition balance calculation means for calculating a nutrition balance on the basis of the acquired nutrition component data and health care data, advice generation means for generating meal advice for the user on the basis of a calculation result, and notification means for transmitting the generated advice to a terminal of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] "The problem to be solved" and "Means to solve the problem"

[0005] In an age of declining birthrates, aging populations, and a 100-year life expectancy, how many people can maintain their health and prevent illness is a major issue. However, maintaining a balanced diet is not easy. Parental interest in food has a major impact, with the level of nutritional education differing from household to household. Under these circumstances, there are limits to how much each individual can check and examine their own diet on a regular basis. Furthermore, there is a risk that easily accessible, inexpensive, and delicious foods and beverages will lead to overeating and malnutrition. To solve these issues, a system that approaches the issue from the perspective of preventing illness is needed. [Means for solving the problem]

[0006] The present invention is a system in which a user takes a photo of food or drink and sends the image to a server. The server is provided with an image analysis means for analyzing the sent photo and identifying the type and amount of food or drink. It also includes a nutritional data acquisition means for acquiring nutritional component data for the identified food or drink by referencing a food database. It also includes a healthcare data acquisition means for acquiring pre-registered user healthcare data and a nutritional balance calculation means for calculating nutritional balance based on this data. It also includes an advice generation means for generating dietary advice for the user based on the calculation results and a notification means for sending the advice to the user's terminal. This allows users to easily select nutritionally balanced meals on a daily basis.

[0007] Definitions of important terms contained in the claims

[0008] "User" refers to an individual who uses this system.

[0009] "Terminal Means" refers to a smartphone, tablet, or other electronic device used by a User.

[0010] "Transmission means" refers to the functions and processes for transmitting data from a terminal to a server.

[0011] "Server" refers to a computer system that receives, analyzes, and processes data sent by users.

[0012] "Image analysis means" refers to algorithms or software used to identify the type and quantity of food and drink from the submitted photograph.

[0013] "Food database" refers to a database that stores nutritional information for various foods and beverages.

[0014] "Nutrition data acquisition means" refers to the functions and processes for acquiring nutritional data of identified foods and beverages from the food database.

[0015] "Healthcare Data" refers to data about your age, gender, health status, and medical history.

[0016] "Healthcare data acquisition means" refers to the functions and processes for acquiring healthcare data of pre-registered users.

[0017] "Nutritional balance calculation means" refers to algorithms or software for calculating nutritional balance based on acquired nutritional component data and healthcare data.

[0018] "Advice generation means" refers to algorithms or software for generating dietary advice for users based on the results of nutritional balance calculations.

[0019] "Notification means" refers to the function or process for sending the generated advice to the user's terminal. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a 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.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0034] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] This invention is a system in which a user takes a photo of food or drink and sends the image to a server, which analyzes the image to identify the type and amount of food or drink, calculates the nutritional balance, and provides appropriate dietary advice.

[0042] Overall system configuration

[0043] The system mainly consists of three parts: the user's terminal, the server, and the database.

[0044] Users' devices, such as smartphones or tablets, have the ability to take photos of food and drinks and send the images to a server via a dedicated app.

[0045] The server analyzes the images, identifies the type and amount of food and drink, obtains nutritional data, compares it with the user's health care data to calculate nutritional balance, and generates dietary advice.

[0046] The database stores nutritional data for various foods and beverages, as well as healthcare data for each user.

[0047] Details of each function

[0048] 1. Take and send a photo

[0049] Users can take photos of food and drink using their smartphones or tablets, and then use a dedicated app to send the photos to the server.

[0050] For example, a user takes a photo of a vegetable salad and grilled chicken for lunch and uploads it to the server via the app.

[0051] 2. Image analysis and calculation of nutritional balance

[0052] The server analyzes the image sent and uses image analysis algorithms to identify the type and quantity of food and drink.

[0053] For example, deep learning techniques can be used to identify vegetable salad and grilled chicken from images.

[0054] Next, the system retrieves nutritional data for the identified food and drink from a food database, such as the vitamin C content of a vegetable salad or the protein content of grilled chicken.

[0055] 3. Matching with healthcare data

[0056] The server obtains the user's pre-registered healthcare data, including the user's age, gender, health status, and medical history.

[0057] The system calculates nutritional balance based on nutritional component data and health care data. For example, if a user has an iron deficiency, that deficiency will be reflected in the analysis results.

[0058] 4. Generating and notifying dietary advice

[0059] Based on the calculation results, the server generates dietary advice for the user, such as "We recommend adding spinach to supplement iron."

[0060] The advice is sent to the user's device, and the user can review the advice within the app and incorporate it into their next meal plan.

[0061] Specific examples

[0062] Example 1:

[0063] User A takes a photo of vegetable salad and grilled chicken for lunch. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies the vegetable salad and grilled chicken. It then retrieves the nutritional information for each item from a food database and compares it with User A's healthcare data. If it determines that User A is iron deficient, the server generates advice and notifies the user to add spinach and liver.

[0064] Example 2:

[0065] User B takes a photo of yogurt and fruit for breakfast. When the photo is sent to the server, the server analyzes the image and identifies the yogurt and fruit. The server then retrieves their nutritional information from a food database and compares it with User B's healthcare data to calculate nutritional balance. If the server determines that User B is calcium deficient, it generates and notifies User B with advice to add cheese or milk to supplement calcium.

[0066] In this way, the system is designed to allow users to easily check the nutritional balance of their meals and receive dietary advice tailored to their health condition, thereby improving the quality of their daily diet and preventing illness.

[0067] The processing flow will be explained below.

[0068] Program processing flow and specific operations

[0069] System-wide processing steps

[0070] Step 1:

[0071] Users use their devices to take photos of food and beverages, and it is important to use the camera app on their smartphone or tablet to capture clear images.

[0072] Step 2:

[0073] The user launches a dedicated app and takes a photo, sends it to the server, and the app packages the photo data as an HTTP request and uploads it to the server.

[0074] Step 3:

[0075] The server receives the uploaded photo data and temporarily stores it in a database, along with metadata (such as the time of submission, user ID, etc.).

[0076] Step 4:

[0077] The server runs an image analysis algorithm to analyze the stored photo data, which uses a deep learning model (e.g., CNN) to identify the type and quantity of food and drink.

[0078] Step 5:

[0079] The server retrieves nutritional information from the food database based on the identified food and drink data, for example, by querying the vitamin C content of a specified vegetable salad or the protein content of grilled chicken.

[0080] Step 6:

[0081] The server retrieves the user's healthcare data, including the user's age, gender, health status, and medical history, and uses a database query to retrieve pre-registered information.

[0082] Step 7:

[0083] The server calculates nutritional balance based on the acquired nutritional data and health care data. For example, if the user has an iron deficiency, the nutrition calculation will include that deficiency.

[0084] Step 8:

[0085] The server generates dietary advice for the user based on the calculation results, such as "We recommend adding spinach to supplement iron."

[0086] Step 9:

[0087] The server sends the generated advice to the user's device, where it is delivered as a push notification or in-app message.

[0088] Step 10:

[0089] Users can receive advice notifications on their device, open the app to view details, and take action to incorporate the advice into their next meal plan.

[0090] Through these steps, the system analyzes the user's diet and provides nutritional advice based on individual healthcare data to support a healthy diet.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] In today's busy lifestyles, many people face the challenge of properly managing their diet and nutritional balance. In particular, those who are deficient in certain nutrients or have a history of such deficiencies require appropriate nutritional management to improve their health. However, manually calculating nutritional information and generating dietary advice is cumbersome and requires specialized knowledge. Therefore, there is a need for a system that allows users to easily and efficiently manage their diet and receive appropriate dietary advice.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes an image acquisition means, a data transmission means, an image analysis means, an information acquisition means, a data acquisition means, a nutritional balance calculation means, a guidance generation means, and a data notification means. Based on image data of food and drink photographed by a user, the server automatically identifies the type and quantity of food and drink, acquires nutritional information, calculates nutritional balance based on the user's health management data, and generates and notifies the user of appropriate dietary guidance. This allows users to efficiently manage their eating habits and receive dietary guidance tailored to their health condition.

[0096] 1. "User" means an individual who uses the system to take photos of their own food and drink and receive dietary advice.

[0097] 2. "Terminal" means a smartphone, tablet, or other device with an image capture function that transmits image data captured by the user to the server.

[0098] 3. "Image acquisition means" refers to the functions and applications that allow users to take images of food and beverages using a terminal.

[0099] 4. "Data transmission means" refers to the communication protocol and functions for transmitting image data captured by the terminal to the server.

[0100] 5. "Server" means a computer system that analyzes received image data, identifies the type and quantity of food and beverages, calculates nutritional balance, and generates dietary advice.

[0101] 6. "Image analysis means" means software and algorithms that analyze image data within the server and identify the type and quantity of food and beverages.

[0102] 7. "Food Information Database" refers to a database that records nutritional information for various foods and beverages.

[0103] 8. "Information acquisition means" refers to the function or process of acquiring nutritional information of the food or beverage identified by the image analysis means from the food information database.

[0104] 9. "Health Management Data" means information about a user's health, such as the user's age, gender, health status, and medical history.

[0105] 10. "Data Capture Method" means a function or process for capturing pre-recorded user health management data.

[0106] 11. "Nutritional balance calculation means" refers to software or algorithms for calculating a user's nutritional balance based on acquired nutritional information and health management data.

[0107] 12. "Guidance generation means" refers to the functions and processes for generating specific dietary advice for the user based on the calculation results of the nutritional balance calculation means.

[0108] 13. "Data notification means" refers to the communication protocols and functions for sending the generated dietary advice to the user's terminal and notifying the user.

[0109] This invention is a system in which a user takes an image of food and drink and sends the image to a server, which analyzes the image to identify the type and quantity of food and drink, calculates the nutritional balance, and provides appropriate dietary advice.

[0110] The entire system is mainly composed of a user terminal, a server, a food information database, and a health management database. Specifically, the roles and processes of each component are as follows:

[0111] User's device

[0112] The user's device is a device such as a smartphone or tablet. A dedicated app is installed on this device and has the following functions:

[0113] 1. Image acquisition method: Users use this dedicated app to take images of food and drink.

[0114] 2. Data transmission means: The captured image data is transmitted to the server using the HTTPS protocol.

[0115] server

[0116] The server analyzes the received image data using the following various means and provides dietary advice to the user.

[0117] 1. Image analysis means: The server analyzes the received image data using a deep learning algorithm (e.g., TensorFlow or PyTorch) to identify the type and quantity of food and beverages.

[0118] 2. Information acquisition means: The server refers to the food information database and acquires nutritional information of the identified food or drink.

[0119] 3. Data acquisition means: The server acquires the user's health management data, including age, gender, health status, and medical history, from the database.

[0120] 4. Nutritional balance calculation means: The server calculates nutritional balance based on the acquired nutritional component information and health management data.

[0121] 5. Guidance generation means: Based on the calculation results, the server generates specific dietary advice for the user.

[0122] 6. Data notification method: The generated dietary advice is sent to the user's device via a dedicated app.

[0123] Database

[0124] 1. Food information database: A database that records nutritional information for various foods and beverages.

[0125] 2. Health management database: A database that records information about the user's health.

[0126] Specific examples

[0127] 1. Example 1:

[0128] User A takes image data of vegetable salad and grilled chicken for lunch.

[0129] Image data is sent to the server using a dedicated app.

[0130] The server analyzes the image and identifies the vegetable salad and grilled chicken. It then retrieves the nutritional information for each from a food information database and compares it with User A's health management data.

[0131] The server confirms that User A is iron deficient, generates dietary advice such as "We recommend adding spinach or liver to supplement your iron intake," and notifies the user's device.

[0132] 2. Example 2:

[0133] User B takes an image of yogurt and fruit for breakfast.

[0134] Image data is sent to the server using a dedicated app.

[0135] The server analyzes the image, identifies the yogurt and fruit, retrieves their nutritional information from a food information database, and compares it with User B's health management data to calculate the nutritional balance.

[0136] The server confirms that User B is calcium deficient, generates dietary advice such as "We recommend adding cheese or milk to supplement calcium," and notifies the user's device.

[0137] Example prompts (for generative AI models)

[0138] "I took a photo of my lunch and uploaded it. The analysis showed that it was a vegetable salad and grilled chicken. Please tell me the detailed nutritional balance."

[0139] "I sent a photo of my breakfast: yogurt and fruit. Please provide dietary advice that takes my health condition into consideration."

[0140] This system allows users to easily manage their eating habits and receive dietary advice based on their health condition, thereby improving the quality of their daily eating habits and achieving proper nutritional management.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] Users take images of food and drink using a dedicated app on their smartphone or tablet. The input is image data of the food and drink, and the output is the image data. Specifically, the user takes a photo of their lunch and saves the image data in JPEG format.

[0144] Step 2:

[0145] The device sends the captured image data to the server using the HTTPS protocol. The input is the captured image data, and the output is the transmission of the image data to the server. For example, the device uploads the captured image to the server via a dedicated app.

[0146] Step 3:

[0147] The server analyzes the received image data. Using a deep learning model (e.g., TensorFlow or PyTorch), it identifies the type and quantity of food and drink from the image. The input is the sent image data, and the output is the type and quantity of identified food and drink. Specifically, it recognizes vegetable salad and grilled chicken through image analysis.

[0148] Step 4:

[0149] The server references the food information database to obtain nutritional information for the identified food or drink. The input is the type of food or drink identified, and the output is the obtained nutritional information. For example, the vitamin C content of a vegetable salad or the protein content of grilled chicken is obtained.

[0150] Step 5:

[0151] The server refers to the health management database to obtain the user's health management data. The input is the user's identification information, and the output is the user's health management data. For example, the server obtains information about User A's age, gender, and iron deficiency.

[0152] Step 6:

[0153] The server calculates nutritional balance based on the acquired nutritional information and health management data. The input is nutritional information and health management data, and the output is the calculated nutritional balance. For example, taking into account User A's iron deficiency, the server comprehensively evaluates the nutritional data of the vegetable salad and grilled chicken they ate.

[0154] Step 7:

[0155] The server generates specific dietary advice for the user based on the calculation results. The input is the calculation result of nutritional balance, and the output is the generated dietary advice. Specifically, it generates advice such as "We recommend adding spinach or liver to supplement iron."

[0156] Step 8:

[0157] The device receives the dietary advice sent from the server and displays it in the dedicated app. The input is the dietary advice from the server, and the output is what is displayed to the user. The user receives a push notification and can check the specific dietary advice in the dedicated app.

[0158] (Application example 1)

[0159] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0160] Many people today suffer from poor eating habits and a lack of nutritional balance, making nutritional management difficult, especially when eating out frequently at cafes and restaurants. Furthermore, there is a lack of systems that allow users to receive accurate dietary advice tailored to their individual health conditions. It is necessary to provide a support system that solves this problem and enables users to choose healthy meals even when eating out.

[0161] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0162] In this invention, the server includes a terminal means for a user to take photos of food and beverages, a transmission means for transmitting the photographed photos to the server, an image analysis means in the server for analyzing the transmitted photos and identifying the type and amount of food and beverages, a nutritional data acquisition means for acquiring nutritional data of the identified food and beverages by referring to a food database, a healthcare data acquisition means for acquiring health care data of the user registered in advance, a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional data and the healthcare data, an advice generation means for generating dietary advice for the user based on the calculation results, a notification means for transmitting the generated advice to the user's terminal, and a supplemental food suggestion means for analyzing photos of food and beverages taken by the user, retrieving related supplemental nutritional information from an external database based on the acquired nutritional data, and suggesting foods that optimize nutritional balance. This allows users to receive dietary advice suited to their own health condition even when eating out, helping them to achieve a nutritionally balanced diet.

[0163] "Terminal means" refers to a device that a user uses to take a photo of food or drink and send the photo to the server. Specifically, it refers to a mobile information terminal such as a smartphone or tablet.

[0164] "Transmission means" refers to the function or process for sending photos taken by the user to the server, including internet communication functions and send buttons within the application.

[0165] "Image analysis means" refers to the technology or algorithms used to analyze the submitted photograph and identify the type and quantity of food or drink, including image recognition technology using deep learning.

[0166] "Nutrition data acquisition means" refers to a function that acquires nutritional component data of the identified food or drink by referring to a food database.

[0167] "Means for obtaining health care data" refers to the function of obtaining the user's pre-registered health care data (age, gender, health condition, medical history, etc.).

[0168] "Nutritional balance calculation means" refers to the technology or algorithm for calculating nutritional balance based on acquired nutritional component data and healthcare data.

[0169] "Advice generation means" refers to a function that generates dietary advice for the user based on the results of nutritional balance calculations.

[0170] "Notification means" refers to a function for sending the generated advice to the user's terminal and notifying the user.

[0171] "Supplementary food suggestion means" refers to a function that analyzes photos of food and beverages taken by the user, obtains related supplementary nutritional information from an external database based on the acquired nutritional data, and suggests foods that will optimize nutritional balance.

[0172] The system that realizes this application example allows the user to take a photo of food or drink and send the image to a server, which then analyzes the image to identify the type and amount of food or drink, calculates the nutritional balance, and provides appropriate dietary advice.

[0173] System configuration

[0174] The system consists of three main parts: the user's terminal, the server, and the database.

[0175] 1. User's Device

[0176] Users take photos of food and drink using a mobile information terminal such as a smartphone and send the photos to the server via a dedicated application, which then uses its internet communication function to send the photo data to the server.

[0177] 2. Server

[0178] Image analysis methods

[0179] The server receives the image and analyzes it using deep learning techniques, including libraries such as TensorFlow and Keras, to identify the type and quantity of food and drink.

[0180] Nutritional data acquisition method

[0181] The server references a food database based on the identified food and drink to obtain nutritional information, which may also be obtained from an external nutrition information API (e.g., "nutrition-api.com").

[0182] Healthcare data acquisition method

[0183] The server acquires the user's pre-registered health care data (age, gender, health condition, medical history, etc.), which is stored in a database.

[0184] Nutritional Balance Calculator

[0185] The server calculates nutritional balance based on the acquired nutritional data and healthcare data, using an algorithm created in Python or other programs.

[0186] Advice Generation Method

[0187] The server generates dietary advice based on the calculation results. The advice generated suggests nutritional supplements tailored to the user's health condition. For example, if the user is iron deficient, it will recommend adding spinach and liver.

[0188] Notification means

[0189] The server then sends the generated advice to the user's device via a dedicated application.

[0190] Supplementary food suggestion means

[0191] The server analyzes photos of food and drink taken by the user, retrieves relevant supplemental nutritional information from an external database based on the acquired nutritional data, and suggests foods to optimize nutritional balance, taking into account the user's health care data.

[0192] Specific examples

[0193] Specific examples are shown below.

[0194] 1. Example 1: A user takes a photo of "chicken salad" at a cafe. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies it as "chicken salad." The server then retrieves the nutritional data for "chicken salad" from the nutrition information API and identifies the user's nutritional deficiency (e.g., iron deficiency). The server generates advice recommending "add spinach" to supplement iron and notifies the user's device.

[0195] Prompt Sentence Examples

[0196] "Please analyze photos of food and drink provided by the user and identify the type of food and drink. Retrieve nutritional information for the identified food and drink from an external API, and match it with the user's health data to suggest specific foods to supplement any nutrient deficiencies."

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] The user takes a photo of the food or drink.

[0200] Input: Image data captured by a user using a mobile information device such as a smartphone.

[0201] Output: Captured image data.

[0202] Specific actions: A user takes a photo of a "chicken salad" at a cafe. Using the camera application on their smartphone, they take a photo that captures the entire food item.

[0203] Step 2:

[0204] The captured photo is sent to the server.

[0205] Input: Captured image data.

[0206] Output: Image data sent to the server.

[0207] Specific operation: The user opens the dedicated application and presses the "send button" to send the photo they have taken to the server. The application then uploads the photo to the server via the Internet.

[0208] Step 3:

[0209] The server analyzes the photos sent and identifies the type and quantity of food and drink.

[0210] Input: Image data sent to the server.

[0211] Output: Data on the type and quantity of food and drink consumed.

[0212] How it works: The server analyzes the received image data using a deep learning model (e.g., using TensorFlow or Keras). The model identifies the type of food in the image (e.g., "chicken salad") and estimates its quantity.

[0213] Step 4:

[0214] The server refers to the food database to obtain nutritional component data of the identified food or drink.

[0215] Input: Data about the type of food or drink.

[0216] Output: Nutritional information for food and drink.

[0217] Specific behavior: The server requests and obtains the nutritional information for the specified food or drink from a food database or an external nutrition information API (e.g., "nutrition-api.com"). For example, it obtains the calorie and macronutrient data for "chicken salad."

[0218] Step 5:

[0219] The server acquires healthcare data of pre-registered users.

[0220] Input: User's identity information.

[0221] Output: User's healthcare data.

[0222] Specific operation: The server accesses the database and retrieves healthcare data (age, gender, health status, medical history, etc.) based on the user's ID. For example, the healthcare data may confirm that the user is iron deficient.

[0223] Step 6:

[0224] The server calculates nutritional balance based on the acquired nutritional component data and health care data.

[0225] Input: Nutritional information of food and drink and user's healthcare data.

[0226] Output: Nutritional balance data.

[0227] How it works: The server uses programs such as Python to compare the nutritional information of food and drink with the user's health data and calculate nutrient deficiencies or excesses. For example, it calculates the iron deficiency that occurs when eating chicken salad alone.

[0228] Step 7:

[0229] The server generates dietary advice for the user based on the calculation results.

[0230] Input: Nutritional balance data.

[0231] Output: Dietary advice.

[0232] Specific action: The server generates advice suggesting specific foods to supplement the missing nutrients, for example, recommending "add spinach to supplement iron."

[0233] Step 8:

[0234] The server sends the generated advice to the user's terminal for notification.

[0235] Enter: dietary advice.

[0236] Output: An advisory notification that is displayed on the user's device.

[0237] How it works: The server sends advice to the user's smartphone via a dedicated application, and the user can then check the advice within the app.

[0238] Step 9:

[0239] The server analyzes photos of food and drink taken by the user, retrieves relevant supplemental nutritional information from an external database based on the acquired nutritional data, and suggests foods that will optimize nutritional balance.

[0240] Input: Food and beverage type data and nutritional information.

[0241] Output: Food suggestion data to optimize nutritional balance.

[0242] Specific operation: Based on the analysis results, the server retrieves food information corresponding to the missing nutrients from an external database (e.g., "nutrition-api.com") and suggests supplementary foods (e.g., "spinach" to supplement iron).

[0243] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0244] This invention combines a system that takes photos of food and drink, analyzes the transmitted images to calculate nutritional balance, and provides personalized dietary advice with an emotion engine that recognizes the user's emotions and customizes dietary advice based on their emotional state.

[0245] Overall system configuration

[0246] The system mainly consists of four parts: the user's terminal, the server, the database, and the emotion engine.

[0247] Users' devices, such as smartphones or tablets, have the ability to take photos of food and drinks and send the images to a server via a dedicated app.

[0248] The server analyzes the images, identifies the type and amount of food and drink, obtains nutritional data, compares it with the user's health care data and emotional state to calculate nutritional balance and generate dietary advice.

[0249] The database stores nutritional data for various foods and beverages, as well as healthcare data for each user.

[0250] The emotion engine has the function of analyzing the user's emotional state and providing that information to the server.

[0251] Details of each function

[0252] 1. Take and send a photo

[0253] Users can take photos of food and drink using their smartphones or tablets, and then use a dedicated app to send the photos to the server.

[0254] For example, a user takes a photo of a vegetable salad and grilled chicken for lunch and uploads it to the server via the app.

[0255] 2. Image analysis and calculation of nutritional balance

[0256] The server analyzes the image sent and uses image analysis algorithms to identify the type and quantity of food and drink.

[0257] For example, deep learning techniques can be used to identify vegetable salad and grilled chicken from images.

[0258] 3. Obtaining nutritional data

[0259] The server retrieves nutritional data for the identified food or drink from a food database, such as the vitamin C content of a vegetable salad or the protein content of grilled chicken.

[0260] 4. Healthcare data acquisition and collation

[0261] The server obtains the user's pre-registered healthcare data, including the user's age, gender, health status, and medical history.

[0262] Nutritional balance is calculated based on nutritional component data and health care data. For example, if a user has an iron deficiency, the nutritional calculation will include that deficiency.

[0263] 5. Acquiring and matching emotion data

[0264] The emotion engine recognizes the user's emotional state and sends that information to the server using the user's voice input, facial expressions, or other biometric data.

[0265] For example, voice recognition technology can be used to analyze the tone and pace of a user's voice to identify their emotional state.

[0266] 6. Generating and customizing dietary advice

[0267] The server generates optimal dietary advice for the user based on the nutritional balance calculation results and emotional state.

[0268] For example, if a user is feeling stressed, the system generates advice recommending foods that have a relaxing effect.

[0269] 7. Notices and Displays

[0270] The server sends the generated advice to the user's device, where it is delivered as a push notification or in-app message.

[0271] Users can receive advice notified on their device, open the app to check the details, and incorporate them into their next meal plan.

[0272] Specific examples

[0273] Example 1:

[0274] User A takes a photo of vegetable salad and grilled chicken for lunch. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies the vegetable salad and grilled chicken. It then retrieves the nutritional information for each item from a food database and compares it with User A's healthcare data. The emotion engine then recognizes User A's emotional state and sends it to the server. The server, having determined that User A is feeling stressed, generates and notifies User A of advice recommending foods that have a relaxing effect.

[0275] Example 2:

[0276] User B takes a photo of yogurt and fruit for breakfast. When the photo is sent to the server, the server analyzes the image and identifies the yogurt and fruit. It then retrieves their nutritional information from a food database and compares it with User B's healthcare data to calculate nutritional balance. The emotion engine then recognizes User B's emotional state and sends it to the server. The server, having confirmed that User B is feeling happy, generates and notifies User B of advice recommending foods to help maintain that mood.

[0277] The system is designed to allow users to easily check the nutritional balance of their meals and receive dietary advice tailored to their health and emotional state, helping to improve the quality of their daily diet and prevent illness.

[0278] The processing flow will be explained below.

[0279] Program processing flow and specific operations

[0280] System-wide processing steps

[0281] Step 1:

[0282] The user uses the device to take a photo of the food or drink. The user launches the camera app on their smartphone or tablet and takes a photo of the food or drink.

[0283] Step 2:

[0284] The user launches the dedicated app and sends the photos they have taken to the server, which then uploads the photo data to the server in the form of an HTTP request.

[0285] Step 3:

[0286] The server receives the uploaded photo data and temporarily stores it in a database, while also recording metadata such as the time of submission and user ID.

[0287] Step 4:

[0288] The server runs an image analysis algorithm, using a deep learning model (e.g., CNN) to identify the type and quantity of food and drink. The identified type and quantity are then stored in a database.

[0289] Step 5:

[0290] The server queries a food database to obtain nutritional information for the identified food or drink, such as the vitamin C content of a vegetable salad or the protein content of grilled chicken.

[0291] Step 6:

[0292] The server retrieves the healthcare data of pre-registered users, using a database query to obtain a dataset containing the user's age, gender, health status, and medical history.

[0293] Step 7:

[0294] The server calculates nutritional balance based on the acquired nutritional data and health care data. For example, if the user has an iron deficiency, the nutrition calculation will take that deficiency into account.

[0295] Step 8:

[0296] The emotion engine recognizes the user's emotional state. Emotion recognition algorithms are run to analyze the user's voice input, facial expressions, or other biometric data.

[0297] Step 9:

[0298] The emotion engine sends the user's recognized emotion data to the server, along with metadata about the user's emotional state.

[0299] Step 10:

[0300] The server combines the nutritional balance calculation results with data from the emotion engine. Taking into account the user's emotional state, it generates appropriate dietary advice. For example, it recommends foods with a relaxing effect to a user who is feeling stressed.

[0301] Step 11:

[0302] The server then sends the generated dietary advice to the user's device in the form of a push notification or an in-app message.

[0303] Step 12:

[0304] Users can receive advice notifications on their device, open the app to view details, and use the advice to plan their next meal.

[0305] Through these steps, the system analyzes the user's diet and provides customized nutritional advice based on individual healthcare and emotional data, supporting a healthy diet.

[0306] Example 2

[0307] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0308] Conventional dietary advice systems provide advice based only on the nutritional balance of foods and beverages, making it difficult to provide detailed dietary advice that reflects the user's emotional state. Therefore, there is a need for a method that generates optimal dietary advice that reflects the user's emotional state and stress level, thereby improving the user's overall health.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0310] In this invention, the server includes emotion analysis means for analyzing the emotional state of the user, nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional component data and biological information, and advice generation means for generating dietary advice for the user based on the nutritional balance calculation result and the emotional state, thereby making it possible to provide more precise and personalized dietary advice that also takes the emotional state of the user into consideration.

[0311] "User" refers to a person using this system.

[0312] "Terminal means" refers to an electronic device that allows a user to take pictures of food and drink and send them to the server.

[0313] "Photographing means" refers to a function that allows a user to photograph an image using a terminal means.

[0314] "Transmission means" refers to a function for transmitting captured images to a server.

[0315] "Server" refers to the computer system that analyzes images sent by users, acquires and processes various data, and generates dietary advice.

[0316] "Image analysis means" refers to a function that analyzes the transmitted image and identifies the type and amount of food and drink.

[0317] "Food database" refers to a database that stores nutritional information for various foods and beverages.

[0318] "Nutrition data acquisition means" refers to a function for acquiring nutritional component data of a specified food or drink from a food database.

[0319] "Biometric data acquisition means" refers to a function that acquires biometric information such as the age, gender, health condition, and medical history of a pre-registered user.

[0320] "Nutritional balance calculation means" refers to a function that calculates nutritional balance based on the acquired nutritional component data and the user's biological information.

[0321] "Emotion analysis means" refers to a function that recognizes the user's emotional state and provides that data to the server.

[0322] "Advice generation means" refers to a function that generates optimal dietary advice for a user based on the nutritional balance calculation results and emotional state.

[0323] "Notification means" refers to a function that sends the generated advice to the user's terminal.

[0324] MODE FOR CARRYING OUT THE INVENTION

[0325] This invention is a system that allows users to take photos of food and drink and send them to a server, which uses image analysis algorithms to calculate nutritional balance and provide personalized dietary advice. Furthermore, it can recognize the user's emotional state and use that information to better customize dietary advice.

[0326] Overall system configuration

[0327] The system mainly consists of four parts: the user's terminal, the server, the database, and the sentiment analysis engine.

[0328] 1. User's Device

[0329] The user's device is a smartphone or tablet, which has the ability to take pictures of food and drink and send them to the server via a dedicated app that is equipped with a camera function and a send button.

[0330] 2. Server

[0331] The server is responsible for image analysis, data acquisition, advice generation and notification functions.

[0332] Deep learning technologies such as TensorFlow are used for image analysis.

[0333] The Python NumPy library is used to match biological data with nutritional data and calculate nutritional balance.

[0334] For emotion recognition, we use the Google Speech-to-Text API and other tools to extract emotions from voice.

[0335] 3. Database

[0336] The database stores nutritional data for various foods and beverages, as well as biometric information for each user (age, gender, health status, medical history).

[0337] For example, obtain nutritional data such as protein and vitamin C for grilled chicken from a food database.

[0338] 4. Sentiment Analysis Engine

[0339] The emotion analysis engine has the function of recognizing the user's emotional state from their voice input and facial expression recognition, and providing that information to the server.

[0340] This allows the system to provide dietary advice based on the user's emotional state, such as stress or happiness.

[0341] Specific examples

[0342] Example 1: User A takes a photo of vegetable salad and grilled chicken for lunch. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies the vegetable salad and grilled chicken. The server then retrieves the nutritional information for each item from the food database and compares it with User A's biometric information to calculate the nutritional balance. Furthermore, the emotion analysis engine recognizes User A's emotional state and confirms that he or she is feeling stressed. The server generates advice recommending foods with a relaxing effect and sends it to the user via push notification.

[0343] Example 2: User B takes a photo of yogurt and fruit for breakfast. The photo sent to the server is analyzed and identified as yogurt and fruit. Nutritional information is then retrieved from the food database and matched with User B's biometric information. The sentiment analysis engine recognizes User B's happy emotional state and generates advice recommending foods to maintain that state, notifying the user via an in-app message.

[0344] This allows users to easily check the nutritional balance of their meals and receive dietary advice appropriate to their health and emotional state.

[0345] Prompt Sentence Examples

[0346] "You take a photo of your food or drink and send the image to our server. The system then analyzes the image and provides dietary advice based on your nutritional balance and emotional state."

[0347] The system allows users to better manage their health and make dietary choices that suit their emotional state.

[0348] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0349] Program processing flow

[0350] Step 1: Take and send a photo

[0351] Users take photos of food and drinks using their smartphones or tablets.

[0352] Specific operation: The user taps the "Take a photo" button in the dedicated app to launch the camera, takes a photo of the food or drink, and presses the "Send" button.

[0353] Input: A photo of food or drink taken by the user

[0354] Output: Food and drink images sent to the server

[0355] Step 2: Receiving and analyzing images

[0356] The server receives the image sent by the user and analyzes it using image analysis algorithms.

[0357] What it does: The server uses an image analysis library (e.g., TensorFlow) to identify food and drink objects in the image and determine their type and quantity.

[0358] Input: Food and drink images sent from the user's device

[0359] Output: Analyzed food and drink types and quantities

[0360] Step 3: Obtaining nutritional data

[0361] Based on the results of the image analysis, the server retrieves nutritional data for the identified food and beverage from a food database.

[0362] Specific operation: The server queries the food database to obtain nutritional information for food and beverage items (e.g., grilled chicken, vegetable salad, etc.).

[0363] Input: Type and quantity of food and drink analyzed

[0364] Output: Nutritional information

[0365] Step 4: Acquire biometric data

[0366] The server obtains the biometric data of pre-registered users, including age, gender, health status, and medical history.

[0367] Specific operation: The server retrieves the user's biometric information from the user database using an SQL query.

[0368] Input: User's ID

[0369] Output: User biometric data

[0370] Step 5: Calculate your nutritional balance

[0371] The server calculates the nutritional balance based on the acquired nutritional component data and biological information.

[0372] Specific operation: The server combines nutritional data and biological data and calculates nutritional balance using Python's NumPy library.

[0373] Input: Nutritional information and user biometric data

[0374] Output: Nutritional balance calculation results

[0375] Step 6: Obtaining emotion data

[0376] The emotion analysis engine recognizes the user's emotional state and sends the data to the server, using voice input, facial expressions, and biometric data.

[0377] How it works: Users record voice messages within the app, and the server passes the voice data to an emotion analysis engine to extract emotional states.

[0378] Input: User's voice or facial expression data

[0379] Output: Perceived emotional state of the user

[0380] Step 7: Generate dietary advice

[0381] The server generates optimal dietary advice for the user based on the nutritional balance calculation results and emotional state.

[0382] Specific operation: The server uses an advice generation algorithm to integrate nutritional balance and emotional state to generate customized advice.

[0383] Input: Nutritional balance calculation results and emotional state

[0384] Output: Optimal dietary advice

[0385] Step 8: Notifications and Display

[0386] The server sends the generated advice to the user's device, where it is delivered as a push notification or in-app message.

[0387] How it works: The server sends advice in JSON format to the user's device, which is then received by a dedicated app. The app then uses the notification API to generate a push notification, which the user can tap to display details.

[0388] Enter: Best Dietary Advice

[0389] Output: Advice displayed on the user's terminal

[0390] (Application example 2)

[0391] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0392] Managing employee health is a very important issue in manufacturing sites. Employees' emotional state is also an important factor, as it directly impacts work efficiency and safety. However, there is currently no way for employees to easily check the nutritional balance of their daily meals and receive appropriate dietary advice tailored to their emotional state. A system to improve this situation is needed.

[0393] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a terminal means for a user to take photos of food and beverages, a transmission means for sending the taken photos to the server, an image analysis means for analyzing the sent photos and identifying the type and amount of food and beverages, a nutritional data acquisition means for acquiring nutritional data of the identified food and beverages by referring to a food database, a healthcare data acquisition means for acquiring health care data of a pre-registered user, a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional data and health care data, an emotion analysis means for customizing dietary advice based on the acquired emotion data, an advice generation means for generating dietary advice for the user based on the calculation results and the emotion data, and a notification means for sending the generated advice to the user's terminal. This makes it possible to provide dietary advice optimized for the health and emotional state of employees, thereby improving work efficiency and safety in the factory.

[0394] A "user terminal" is a device that has the function of taking photos of food and drink and sending the images to a server.

[0395] "Transmission means" refers to a method or device that has the function of transmitting a photograph taken from a user's terminal to a server.

[0396] "Image analysis means" refers to an algorithm or device that has the function of analyzing the transmitted photograph and identifying the type and quantity of food and drink.

[0397] The "nutrition data acquisition means" refers to a method or device that acquires nutritional component data of a specified food or drink by referring to a food database.

[0398] The "healthcare data acquisition means" refers to a method or device for acquiring healthcare data of a user who has been registered in advance.

[0399] The "nutritional balance calculation means" refers to an algorithm or device that has the function of calculating nutritional balance based on the acquired nutritional component data and healthcare data.

[0400] The "emotion analysis means" is an algorithm or device that has the function of customizing dietary advice based on acquired emotion data.

[0401] The "advice generation means" refers to a method or device that generates dietary advice for a user based on the calculation results and emotion data.

[0402] The "notification means" is a method or device that has the function of transmitting the generated advice to the user's terminal.

[0403] This invention is implemented as a system for providing dietary advice based on the health management and emotional state of employees in a factory. The system mainly consists of a user terminal, a server, a database, and an emotion engine.

[0404] User's device

[0405] The user's device is a mobile device such as a smartphone that has the function of taking photos of the food and beverages served in the factory cafeteria. A dedicated application is also installed on this device, allowing the user to send the photos to the server.

[0406] server

[0407] The server has the following functions:

[0408] Image analysis means: Deep learning technology is used to analyze photos of food and drink sent from the user's device to identify the type and quantity of food and drink, and to obtain nutritional data for the identified food and drink from a food database.

[0409] Healthcare data acquisition means: Acquires healthcare data of pre-registered users from a database.

[0410] Emotion analysis means: The emotional state of the user is analyzed using an emotion engine based on data such as voice input and facial expressions obtained from the user's device.

[0411] Nutritional balance calculation means: Calculates the user's nutritional balance based on the acquired nutritional data and health care data.

[0412] Advice generation means: Based on the calculation results and the analyzed emotion data, dietary advice is generated for the user.

[0413] Notification method: The generated advice is sent to the user's device in the form of a push notification or similar.

[0414] Database

[0415] The database stores nutritional data for various foods and beverages, as well as healthcare data for each user. The food database consists of multiple lists, each containing detailed information such as the nutritional content and calories of each food.

[0416] Emotion Engine

[0417] The emotion engine uses a deep learning model to recognize the user's emotional state. The engine analyzes the user's emotional state based on their voice tone and facial expression data, and provides this information to the server.

[0418] Specific examples

[0419] For example, an employee takes a photo of the food served in the cafeteria at lunchtime with their smartphone and sends it to a server via a dedicated app. At this time, the user uses voice input to record their emotional state. The server analyzes the sent photo and voice data to identify the type and amount of food and drink and the user's emotional state. It then retrieves the necessary data from a food database and a healthcare database and calculates nutritional balance. It uses an emotion engine to analyze the user's emotional state and generates optimal dietary advice based on that. Finally, the generated advice is pushed to the user's device.

[0420] Prompt Sentence Examples

[0421] Image path: . / lunch.jpg

[0422] Path to the audio data: . / user_voice.wav

[0423] User ID: employee_12345

[0424] In this way, a system can be designed that allows employees to easily check the nutritional balance of their meals and receive dietary advice tailored to their health and emotional state, thereby improving the quality of their daily diet and increasing work efficiency and safety in the factory.

[0425] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0426] Step 1:

[0427] A user takes a photo of food or drink using a mobile device such as a smartphone. At this time, a dedicated application is launched and image data of the food or drink is acquired using the built-in camera. The input is the photo of the food or drink (image data), and the output is the image taken by the user.

[0428] Step 2:

[0429] The user sends the photographic data of the food or drink they have taken to the server via a dedicated application. The input is the image data they have just taken, and the output is the image data sent to the server.

[0430] Step 3:

[0431] The server receives the transmitted photo data and uses an image analysis algorithm to identify the types and quantities of food and drink contained in the photo. The input is the received image data, and the output is a list of identified food and drink items and their quantities. Specific operations include image recognition processing using a deep learning model.

[0432] Step 4:

[0433] The server references a food database to retrieve nutritional data related to the identified foods and beverages. The input is a list of the identified foods and beverages, and the output is the nutritional data corresponding to each food and beverage. Specific operations include retrieving data through a database query.

[0434] Step 5:

[0435] The server retrieves pre-registered user healthcare data from a database. The input is the user's ID information, and the output is the corresponding user's healthcare data. Specific operations include retrieving data through a database query.

[0436] Step 6:

[0437] The server calculates nutritional balance based on the acquired nutritional component data and healthcare data. The input is nutritional component data and healthcare data, and the output is the result of the nutritional balance calculation. Specific operations include comparing the required amount of nutrients with the amount of intake and calculating deficiencies and excesses.

[0438] Step 7:

[0439] Voice input and facial expression data are sent from the user's device, and the server receives the data and analyzes it using an emotion engine. The input is voice data and facial expression data, and the output is the user's emotional state. Specifically, deep learning models for voice recognition and facial expression analysis are used.

[0440] Step 8:

[0441] The server generates dietary advice for the user based on the nutritional balance calculation results and the analyzed emotion data. The input is the nutritional balance calculation results and emotion data, and the output is customized dietary advice. Specific operations include an algorithm that takes both data into account and proposes an optimal meal plan.

[0442] Step 9:

[0443] The server notifies the user's device of the generated advice. The input is the generated dietary advice, and the output is a notification message to the user's device. Specific operations include sending push notifications and in-app messages.

[0444] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0445] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0446] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0447] [Second embodiment]

[0448] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0449] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0450] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0451] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0452] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0453] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0454] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0455] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0456] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0457] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0458] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0459] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0460] This invention is a system in which a user takes a photo of food or drink and sends the image to a server, which analyzes the image to identify the type and amount of food or drink, calculates the nutritional balance, and provides appropriate dietary advice.

[0461] Overall system configuration

[0462] The system mainly consists of three parts: the user's terminal, the server, and the database.

[0463] Users' devices, such as smartphones or tablets, have the ability to take photos of food and drinks and send the images to a server via a dedicated app.

[0464] The server analyzes the images, identifies the type and amount of food and drink, obtains nutritional data, compares it with the user's health care data to calculate nutritional balance, and generates dietary advice.

[0465] The database stores nutritional data for various foods and beverages, as well as healthcare data for each user.

[0466] Details of each function

[0467] 1. Take and send a photo

[0468] Users can take photos of food and drink using their smartphones or tablets, and then use a dedicated app to send the photos to the server.

[0469] For example, a user takes a photo of a vegetable salad and grilled chicken for lunch and uploads it to the server via the app.

[0470] 2. Image analysis and calculation of nutritional balance

[0471] The server analyzes the image sent and uses image analysis algorithms to identify the type and quantity of food and drink.

[0472] For example, deep learning techniques can be used to identify vegetable salad and grilled chicken from images.

[0473] Next, the system retrieves nutritional data for the identified food and drink from a food database, such as the vitamin C content of a vegetable salad or the protein content of grilled chicken.

[0474] 3. Matching with healthcare data

[0475] The server obtains the user's pre-registered healthcare data, including the user's age, gender, health status, and medical history.

[0476] The system calculates nutritional balance based on nutritional component data and health care data. For example, if a user has an iron deficiency, that deficiency will be reflected in the analysis results.

[0477] 4. Generating and notifying dietary advice

[0478] Based on the calculation results, the server generates dietary advice for the user, such as "We recommend adding spinach to supplement iron."

[0479] The advice is sent to the user's device, and the user can review the advice within the app and incorporate it into their next meal plan.

[0480] Specific examples

[0481] Example 1:

[0482] User A takes a photo of vegetable salad and grilled chicken for lunch. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies the vegetable salad and grilled chicken. It then retrieves the nutritional information for each item from a food database and compares it with User A's healthcare data. If it determines that User A is iron deficient, the server generates advice and notifies the user to add spinach and liver.

[0483] Example 2:

[0484] User B takes a photo of yogurt and fruit for breakfast. When the photo is sent to the server, the server analyzes the image and identifies the yogurt and fruit. The server then retrieves their nutritional information from a food database and compares it with User B's healthcare data to calculate nutritional balance. If the server determines that User B is calcium deficient, it generates and notifies User B with advice to add cheese or milk to supplement calcium.

[0485] In this way, the system is designed to allow users to easily check the nutritional balance of their meals and receive dietary advice tailored to their health condition, thereby improving the quality of their daily diet and preventing illness.

[0486] The processing flow will be explained below.

[0487] Program processing flow and specific operations

[0488] System-wide processing steps

[0489] Step 1:

[0490] Users use their devices to take photos of food and beverages, and it is important to use the camera app on their smartphone or tablet to capture clear images.

[0491] Step 2:

[0492] The user launches a dedicated app and takes a photo, sends it to the server, and the app packages the photo data as an HTTP request and uploads it to the server.

[0493] Step 3:

[0494] The server receives the uploaded photo data and temporarily stores it in a database, along with metadata (such as the time of submission, user ID, etc.).

[0495] Step 4:

[0496] The server runs an image analysis algorithm to analyze the stored photo data, which uses a deep learning model (e.g., CNN) to identify the type and quantity of food and drink.

[0497] Step 5:

[0498] The server retrieves nutritional information from the food database based on the identified food and drink data, for example, by querying the vitamin C content of a specified vegetable salad or the protein content of grilled chicken.

[0499] Step 6:

[0500] The server retrieves the user's healthcare data, including the user's age, gender, health status, and medical history, and uses a database query to retrieve pre-registered information.

[0501] Step 7:

[0502] The server calculates nutritional balance based on the acquired nutritional data and health care data. For example, if the user has an iron deficiency, the nutrition calculation will include that deficiency.

[0503] Step 8:

[0504] The server generates dietary advice for the user based on the calculation results, such as "We recommend adding spinach to supplement iron."

[0505] Step 9:

[0506] The server sends the generated advice to the user's device, where it is delivered as a push notification or in-app message.

[0507] Step 10:

[0508] Users can receive advice notifications on their device, open the app to view details, and take action to incorporate the advice into their next meal plan.

[0509] Through these steps, the system analyzes the user's diet and provides nutritional advice based on individual healthcare data to support a healthy diet.

[0510] Example 1

[0511] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0512] In today's busy lifestyles, many people face the challenge of properly managing their diet and nutritional balance. In particular, those who are deficient in certain nutrients or have a history of such deficiencies require appropriate nutritional management to improve their health. However, manually calculating nutritional information and generating dietary advice is cumbersome and requires specialized knowledge. Therefore, there is a need for a system that allows users to easily and efficiently manage their diet and receive appropriate dietary advice.

[0513] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0514] In this invention, the server includes an image acquisition means, a data transmission means, an image analysis means, an information acquisition means, a data acquisition means, a nutritional balance calculation means, a guidance generation means, and a data notification means. Based on image data of food and drink photographed by a user, the server automatically identifies the type and quantity of food and drink, acquires nutritional information, calculates nutritional balance based on the user's health management data, and generates and notifies the user of appropriate dietary guidance. This allows users to efficiently manage their eating habits and receive dietary guidance tailored to their health condition.

[0515] 1. "User" means an individual who uses the system to take photos of their own food and drink and receive dietary advice.

[0516] 2. "Terminal" means a smartphone, tablet, or other device with an image capture function that transmits image data captured by the user to the server.

[0517] 3. "Image acquisition means" refers to the functions and applications that allow users to take images of food and beverages using a terminal.

[0518] 4. "Data transmission means" refers to the communication protocol and functions for transmitting image data captured by the terminal to the server.

[0519] 5. "Server" means a computer system that analyzes received image data, identifies the type and quantity of food and beverages, calculates nutritional balance, and generates dietary advice.

[0520] 6. "Image analysis means" means software and algorithms that analyze image data within the server and identify the type and quantity of food and beverages.

[0521] 7. "Food Information Database" refers to a database that records nutritional information for various foods and beverages.

[0522] 8. "Information acquisition means" refers to the function or process of acquiring nutritional information of the food or beverage identified by the image analysis means from the food information database.

[0523] 9. "Health Management Data" means information about a user's health, such as the user's age, gender, health status, and medical history.

[0524] 10. "Data Capture Method" means a function or process for capturing pre-recorded user health management data.

[0525] 11. "Nutritional balance calculation means" refers to software or algorithms for calculating a user's nutritional balance based on acquired nutritional information and health management data.

[0526] 12. "Guidance generation means" refers to the functions and processes for generating specific dietary advice for the user based on the calculation results of the nutritional balance calculation means.

[0527] 13. "Data notification means" refers to the communication protocols and functions for sending the generated dietary advice to the user's terminal and notifying the user.

[0528] This invention is a system in which a user takes an image of food and drink and sends the image to a server, which analyzes the image to identify the type and quantity of food and drink, calculates the nutritional balance, and provides appropriate dietary advice.

[0529] The entire system is mainly composed of a user terminal, a server, a food information database, and a health management database. Specifically, the roles and processes of each component are as follows:

[0530] User's device

[0531] The user's device is a device such as a smartphone or tablet. A dedicated app is installed on this device and has the following functions:

[0532] 1. Image acquisition method: Users use this dedicated app to take images of food and drink.

[0533] 2. Data transmission means: The captured image data is transmitted to the server using the HTTPS protocol.

[0534] server

[0535] The server analyzes the received image data using the following various means and provides dietary advice to the user.

[0536] 1. Image analysis means: The server analyzes the received image data using a deep learning algorithm (e.g., TensorFlow or PyTorch) to identify the type and quantity of food and beverages.

[0537] 2. Information acquisition means: The server refers to the food information database and acquires nutritional information of the identified food or drink.

[0538] 3. Data acquisition means: The server acquires the user's health management data, including age, gender, health status, and medical history, from the database.

[0539] 4. Nutritional balance calculation means: The server calculates nutritional balance based on the acquired nutritional component information and health management data.

[0540] 5. Guidance generation means: Based on the calculation results, the server generates specific dietary advice for the user.

[0541] 6. Data notification method: The generated dietary advice is sent to the user's device via a dedicated app.

[0542] Database

[0543] 1. Food information database: A database that records nutritional information for various foods and beverages.

[0544] 2. Health management database: A database that records information about the user's health.

[0545] Specific examples

[0546] 1. Example 1:

[0547] User A takes image data of vegetable salad and grilled chicken for lunch.

[0548] Image data is sent to the server using a dedicated app.

[0549] The server analyzes the image and identifies the vegetable salad and grilled chicken. It then retrieves the nutritional information for each from a food information database and compares it with User A's health management data.

[0550] The server confirms that User A is iron deficient, generates dietary advice such as "We recommend adding spinach or liver to supplement your iron intake," and notifies the user's device.

[0551] 2. Example 2:

[0552] User B takes an image of yogurt and fruit for breakfast.

[0553] Image data is sent to the server using a dedicated app.

[0554] The server analyzes the image, identifies the yogurt and fruit, retrieves their nutritional information from a food information database, and compares it with User B's health management data to calculate the nutritional balance.

[0555] The server confirms that User B is calcium deficient, generates dietary advice such as "We recommend adding cheese or milk to supplement calcium," and notifies the user's device.

[0556] Example prompts (for generative AI models)

[0557] "I took a photo of my lunch and uploaded it. The analysis showed that it was a vegetable salad and grilled chicken. Please tell me the detailed nutritional balance."

[0558] "I sent a photo of my breakfast: yogurt and fruit. Please provide dietary advice that takes my health condition into consideration."

[0559] This system allows users to easily manage their eating habits and receive dietary advice based on their health condition, thereby improving the quality of their daily eating habits and achieving proper nutritional management.

[0560] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0561] Step 1:

[0562] Users take images of food and drink using a dedicated app on their smartphone or tablet. The input is image data of the food and drink, and the output is the image data. Specifically, the user takes a photo of their lunch and saves the image data in JPEG format.

[0563] Step 2:

[0564] The device sends the captured image data to the server using the HTTPS protocol. The input is the captured image data, and the output is the transmission of the image data to the server. For example, the device uploads the captured image to the server via a dedicated app.

[0565] Step 3:

[0566] The server analyzes the received image data. Using a deep learning model (e.g., TensorFlow or PyTorch), it identifies the type and quantity of food and drink from the image. The input is the sent image data, and the output is the type and quantity of identified food and drink. Specifically, it recognizes vegetable salad and grilled chicken through image analysis.

[0567] Step 4:

[0568] The server references the food information database to obtain nutritional information for the identified food or drink. The input is the type of food or drink identified, and the output is the obtained nutritional information. For example, the vitamin C content of a vegetable salad or the protein content of grilled chicken is obtained.

[0569] Step 5:

[0570] The server refers to the health management database to obtain the user's health management data. The input is the user's identification information, and the output is the user's health management data. For example, the server obtains information about User A's age, gender, and iron deficiency.

[0571] Step 6:

[0572] The server calculates nutritional balance based on the acquired nutritional information and health management data. The input is nutritional information and health management data, and the output is the calculated nutritional balance. For example, taking into account User A's iron deficiency, the server comprehensively evaluates the nutritional data of the vegetable salad and grilled chicken they ate.

[0573] Step 7:

[0574] The server generates specific dietary advice for the user based on the calculation results. The input is the calculation result of nutritional balance, and the output is the generated dietary advice. Specifically, it generates advice such as "We recommend adding spinach or liver to supplement iron."

[0575] Step 8:

[0576] The device receives the dietary advice sent from the server and displays it in the dedicated app. The input is the dietary advice from the server, and the output is what is displayed to the user. The user receives a push notification and can check the specific dietary advice in the dedicated app.

[0577] (Application example 1)

[0578] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0579] Many people today suffer from poor eating habits and a lack of nutritional balance, making nutritional management difficult, especially when eating out frequently at cafes and restaurants. Furthermore, there is a lack of systems that allow users to receive accurate dietary advice tailored to their individual health conditions. It is necessary to provide a support system that solves this problem and enables users to choose healthy meals even when eating out.

[0580] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0581] In this invention, the server includes a terminal means for a user to take photos of food and beverages, a transmission means for transmitting the photographed photos to the server, an image analysis means in the server for analyzing the transmitted photos and identifying the type and amount of food and beverages, a nutritional data acquisition means for acquiring nutritional data of the identified food and beverages by referring to a food database, a healthcare data acquisition means for acquiring health care data of the user registered in advance, a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional data and the healthcare data, an advice generation means for generating dietary advice for the user based on the calculation results, a notification means for transmitting the generated advice to the user's terminal, and a supplemental food suggestion means for analyzing photos of food and beverages taken by the user, retrieving related supplemental nutritional information from an external database based on the acquired nutritional data, and suggesting foods that optimize nutritional balance. This allows users to receive dietary advice suited to their own health condition even when eating out, helping them to achieve a nutritionally balanced diet.

[0582] "Terminal means" refers to a device that a user uses to take a photo of food or drink and send the photo to the server. Specifically, it refers to a mobile information terminal such as a smartphone or tablet.

[0583] "Transmission means" refers to the function or process for sending photos taken by the user to the server, including internet communication functions and send buttons within the application.

[0584] "Image analysis means" refers to the technology or algorithms used to analyze the submitted photograph and identify the type and quantity of food or drink, including image recognition technology using deep learning.

[0585] "Nutrition data acquisition means" refers to a function that acquires nutritional component data of the identified food or drink by referring to a food database.

[0586] "Means for obtaining health care data" refers to the function of obtaining the user's pre-registered health care data (age, gender, health condition, medical history, etc.).

[0587] "Nutritional balance calculation means" refers to the technology or algorithm for calculating nutritional balance based on acquired nutritional component data and healthcare data.

[0588] "Advice generation means" refers to a function that generates dietary advice for the user based on the results of nutritional balance calculations.

[0589] "Notification means" refers to a function for sending the generated advice to the user's terminal and notifying the user.

[0590] "Supplementary food suggestion means" refers to a function that analyzes photos of food and beverages taken by the user, obtains related supplementary nutritional information from an external database based on the acquired nutritional data, and suggests foods that will optimize nutritional balance.

[0591] The system that realizes this application example allows the user to take a photo of food or drink and send the image to a server, which then analyzes the image to identify the type and amount of food or drink, calculates the nutritional balance, and provides appropriate dietary advice.

[0592] System configuration

[0593] The system consists of three main parts: the user's terminal, the server, and the database.

[0594] 1. User's Device

[0595] Users take photos of food and drink using a mobile information terminal such as a smartphone and send the photos to the server via a dedicated application, which then uses its internet communication function to send the photo data to the server.

[0596] 2. Server

[0597] Image analysis methods

[0598] The server receives the image and analyzes it using deep learning techniques, including libraries such as TensorFlow and Keras, to identify the type and quantity of food and drink.

[0599] Nutritional data acquisition method

[0600] The server references a food database based on the identified food and drink to obtain nutritional information, which may also be obtained from an external nutrition information API (e.g., "nutrition-api.com").

[0601] Healthcare data acquisition method

[0602] The server acquires the user's pre-registered health care data (age, gender, health condition, medical history, etc.), which is stored in a database.

[0603] Nutritional Balance Calculator

[0604] The server calculates nutritional balance based on the acquired nutritional data and healthcare data, using an algorithm created in Python or other programs.

[0605] Advice Generation Method

[0606] The server generates dietary advice based on the calculation results. The advice generated suggests nutritional supplements tailored to the user's health condition. For example, if the user is iron deficient, it will recommend adding spinach and liver.

[0607] Notification means

[0608] The server then sends the generated advice to the user's device via a dedicated application.

[0609] Supplementary food suggestion means

[0610] The server analyzes photos of food and drink taken by the user, retrieves relevant supplemental nutritional information from an external database based on the acquired nutritional data, and suggests foods to optimize nutritional balance, taking into account the user's health care data.

[0611] Specific examples

[0612] Specific examples are shown below.

[0613] 1. Example 1: A user takes a photo of "chicken salad" at a cafe. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies it as "chicken salad." The server then retrieves the nutritional data for "chicken salad" from the nutrition information API and identifies the user's nutritional deficiency (e.g., iron deficiency). The server generates advice recommending "add spinach" to supplement iron and notifies the user's device.

[0614] Prompt Sentence Examples

[0615] "Please analyze photos of food and drink provided by the user and identify the type of food and drink. Retrieve nutritional information for the identified food and drink from an external API, and match it with the user's health data to suggest specific foods to supplement any nutrient deficiencies."

[0616] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0617] Step 1:

[0618] The user takes a photo of the food or drink.

[0619] Input: Image data captured by a user using a mobile information device such as a smartphone.

[0620] Output: Captured image data.

[0621] Specific actions: A user takes a photo of a "chicken salad" at a cafe. Using the camera application on their smartphone, they take a photo that captures the entire food item.

[0622] Step 2:

[0623] The captured photo is sent to the server.

[0624] Input: Captured image data.

[0625] Output: Image data sent to the server.

[0626] Specific operation: The user opens the dedicated application and presses the "send button" to send the photo they have taken to the server. The application then uploads the photo to the server via the Internet.

[0627] Step 3:

[0628] The server analyzes the photos sent and identifies the type and quantity of food and drink.

[0629] Input: Image data sent to the server.

[0630] Output: Data on the type and quantity of food and drink consumed.

[0631] How it works: The server analyzes the received image data using a deep learning model (e.g., using TensorFlow or Keras). The model identifies the type of food in the image (e.g., "chicken salad") and estimates its quantity.

[0632] Step 4:

[0633] The server refers to the food database to obtain nutritional component data of the identified food or drink.

[0634] Input: Data about the type of food or drink.

[0635] Output: Nutritional information for food and drink.

[0636] Specific behavior: The server requests and obtains the nutritional information for the specified food or drink from a food database or an external nutrition information API (e.g., "nutrition-api.com"). For example, it obtains the calorie and macronutrient data for "chicken salad."

[0637] Step 5:

[0638] The server acquires healthcare data of pre-registered users.

[0639] Input: User's identity information.

[0640] Output: User's healthcare data.

[0641] Specific operation: The server accesses the database and retrieves healthcare data (age, gender, health status, medical history, etc.) based on the user's ID. For example, the healthcare data may confirm that the user is iron deficient.

[0642] Step 6:

[0643] The server calculates nutritional balance based on the acquired nutritional component data and health care data.

[0644] Input: Nutritional information of food and drink and user's healthcare data.

[0645] Output: Nutritional balance data.

[0646] How it works: The server uses programs such as Python to compare the nutritional information of food and drink with the user's health data and calculate nutrient deficiencies or excesses. For example, it calculates the iron deficiency that occurs when eating chicken salad alone.

[0647] Step 7:

[0648] The server generates dietary advice for the user based on the calculation results.

[0649] Input: Nutritional balance data.

[0650] Output: Dietary advice.

[0651] Specific action: The server generates advice suggesting specific foods to supplement the missing nutrients, for example, recommending "add spinach to supplement iron."

[0652] Step 8:

[0653] The server sends the generated advice to the user's terminal for notification.

[0654] Enter: dietary advice.

[0655] Output: An advisory notification that is displayed on the user's device.

[0656] How it works: The server sends advice to the user's smartphone via a dedicated application, and the user can then check the advice within the app.

[0657] Step 9:

[0658] The server analyzes photos of food and drink taken by the user, retrieves relevant supplemental nutritional information from an external database based on the acquired nutritional data, and suggests foods that will optimize nutritional balance.

[0659] Input: Food and beverage type data and nutritional information.

[0660] Output: Food suggestion data to optimize nutritional balance.

[0661] Specific operation: Based on the analysis results, the server retrieves food information corresponding to the missing nutrients from an external database (e.g., "nutrition-api.com") and suggests supplementary foods (e.g., "spinach" to supplement iron).

[0662] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0663] This invention combines a system that takes photos of food and drink, analyzes the transmitted images to calculate nutritional balance, and provides personalized dietary advice with an emotion engine that recognizes the user's emotions and customizes dietary advice based on their emotional state.

[0664] Overall system configuration

[0665] The system mainly consists of four parts: the user's terminal, the server, the database, and the emotion engine.

[0666] Users' devices, such as smartphones or tablets, have the ability to take photos of food and drinks and send the images to a server via a dedicated app.

[0667] The server analyzes the images, identifies the type and amount of food and drink, obtains nutritional data, compares it with the user's health care data and emotional state to calculate nutritional balance and generate dietary advice.

[0668] The database stores nutritional data for various foods and beverages, as well as healthcare data for each user.

[0669] The emotion engine has the function of analyzing the user's emotional state and providing that information to the server.

[0670] Details of each function

[0671] 1. Take and send a photo

[0672] Users can take photos of food and drink using their smartphones or tablets, and then use a dedicated app to send the photos to the server.

[0673] For example, a user takes a photo of a vegetable salad and grilled chicken for lunch and uploads it to the server via the app.

[0674] 2. Image analysis and calculation of nutritional balance

[0675] The server analyzes the image sent and uses image analysis algorithms to identify the type and quantity of food and drink.

[0676] For example, deep learning techniques can be used to identify vegetable salad and grilled chicken from images.

[0677] 3. Obtaining nutritional data

[0678] The server retrieves nutritional data for the identified food or drink from a food database, such as the vitamin C content of a vegetable salad or the protein content of grilled chicken.

[0679] 4. Healthcare data acquisition and collation

[0680] The server obtains the user's pre-registered healthcare data, including the user's age, gender, health status, and medical history.

[0681] Nutritional balance is calculated based on nutritional component data and health care data. For example, if a user has an iron deficiency, the nutritional calculation will include that deficiency.

[0682] 5. Acquiring and matching emotion data

[0683] The emotion engine recognizes the user's emotional state and sends that information to the server using the user's voice input, facial expressions, or other biometric data.

[0684] For example, voice recognition technology can be used to analyze the tone and pace of a user's voice to identify their emotional state.

[0685] 6. Generating and customizing dietary advice

[0686] The server generates optimal dietary advice for the user based on the nutritional balance calculation results and emotional state.

[0687] For example, if a user is feeling stressed, the system generates advice recommending foods that have a relaxing effect.

[0688] 7. Notices and Displays

[0689] The server sends the generated advice to the user's device, where it is delivered as a push notification or in-app message.

[0690] Users can receive advice notified on their device, open the app to check the details, and incorporate them into their next meal plan.

[0691] Specific examples

[0692] Example 1:

[0693] User A takes a photo of vegetable salad and grilled chicken for lunch. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies the vegetable salad and grilled chicken. It then retrieves the nutritional information for each item from a food database and compares it with User A's healthcare data. The emotion engine then recognizes User A's emotional state and sends it to the server. The server, having determined that User A is feeling stressed, generates and notifies User A of advice recommending foods that have a relaxing effect.

[0694] Example 2:

[0695] User B takes a photo of yogurt and fruit for breakfast. When the photo is sent to the server, the server analyzes the image and identifies the yogurt and fruit. It then retrieves their nutritional information from a food database and compares it with User B's healthcare data to calculate nutritional balance. The emotion engine then recognizes User B's emotional state and sends it to the server. The server, having confirmed that User B is feeling happy, generates and notifies User B of advice recommending foods to help maintain that mood.

[0696] The system is designed to allow users to easily check the nutritional balance of their meals and receive dietary advice tailored to their health and emotional state, helping to improve the quality of their daily diet and prevent illness.

[0697] The processing flow will be explained below.

[0698] Program processing flow and specific operations

[0699] System-wide processing steps

[0700] Step 1:

[0701] The user uses the device to take a photo of the food or drink. The user launches the camera app on their smartphone or tablet and takes a photo of the food or drink.

[0702] Step 2:

[0703] The user launches the dedicated app and sends the photos they have taken to the server, which then uploads the photo data to the server in the form of an HTTP request.

[0704] Step 3:

[0705] The server receives the uploaded photo data and temporarily stores it in a database, while also recording metadata such as the time of submission and user ID.

[0706] Step 4:

[0707] The server runs an image analysis algorithm, using a deep learning model (e.g., CNN) to identify the type and quantity of food and drink. The identified type and quantity are then stored in a database.

[0708] Step 5:

[0709] The server queries a food database to obtain nutritional information for the identified food or drink, such as the vitamin C content of a vegetable salad or the protein content of grilled chicken.

[0710] Step 6:

[0711] The server retrieves the healthcare data of pre-registered users, using a database query to obtain a dataset containing the user's age, gender, health status, and medical history.

[0712] Step 7:

[0713] The server calculates nutritional balance based on the acquired nutritional data and health care data. For example, if the user has an iron deficiency, the nutrition calculation will take that deficiency into account.

[0714] Step 8:

[0715] The emotion engine recognizes the user's emotional state. Emotion recognition algorithms are run to analyze the user's voice input, facial expressions, or other biometric data.

[0716] Step 9:

[0717] The emotion engine sends the user's recognized emotion data to the server, along with metadata about the user's emotional state.

[0718] Step 10:

[0719] The server combines the nutritional balance calculation results with data from the emotion engine. Taking into account the user's emotional state, it generates appropriate dietary advice. For example, it recommends foods with a relaxing effect to a user who is feeling stressed.

[0720] Step 11:

[0721] The server then sends the generated dietary advice to the user's device in the form of a push notification or an in-app message.

[0722] Step 12:

[0723] Users can receive advice notifications on their device, open the app to view details, and use the advice to plan their next meal.

[0724] Through these steps, the system analyzes the user's diet and provides customized nutritional advice based on individual healthcare and emotional data, supporting a healthy diet.

[0725] Example 2

[0726] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0727] Conventional dietary advice systems provide advice based only on the nutritional balance of foods and beverages, making it difficult to provide detailed dietary advice that reflects the user's emotional state. Therefore, there is a need for a method that generates optimal dietary advice that reflects the user's emotional state and stress level, thereby improving the user's overall health.

[0728] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0729] In this invention, the server includes emotion analysis means for analyzing the emotional state of the user, nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional component data and biological information, and advice generation means for generating dietary advice for the user based on the nutritional balance calculation result and the emotional state, thereby making it possible to provide more precise and personalized dietary advice that also takes the emotional state of the user into consideration.

[0730] "User" refers to a person using this system.

[0731] "Terminal means" refers to an electronic device that allows a user to take pictures of food and drink and send them to the server.

[0732] "Photographing means" refers to a function that allows a user to photograph an image using a terminal means.

[0733] "Transmission means" refers to a function for transmitting captured images to a server.

[0734] "Server" refers to the computer system that analyzes images sent by users, acquires and processes various data, and generates dietary advice.

[0735] "Image analysis means" refers to a function that analyzes the transmitted image and identifies the type and amount of food and drink.

[0736] "Food database" refers to a database that stores nutritional information for various foods and beverages.

[0737] "Nutrition data acquisition means" refers to a function for acquiring nutritional component data of a specified food or drink from a food database.

[0738] "Biometric data acquisition means" refers to a function that acquires biometric information such as the age, gender, health condition, and medical history of a pre-registered user.

[0739] "Nutritional balance calculation means" refers to a function that calculates nutritional balance based on the acquired nutritional component data and the user's biological information.

[0740] "Emotion analysis means" refers to a function that recognizes the user's emotional state and provides that data to the server.

[0741] "Advice generation means" refers to a function that generates optimal dietary advice for a user based on the nutritional balance calculation results and emotional state.

[0742] "Notification means" refers to a function that sends the generated advice to the user's terminal.

[0743] MODE FOR CARRYING OUT THE INVENTION

[0744] This invention is a system that allows users to take photos of food and drink and send them to a server, which uses image analysis algorithms to calculate nutritional balance and provide personalized dietary advice. Furthermore, it can recognize the user's emotional state and use that information to better customize dietary advice.

[0745] Overall system configuration

[0746] The system mainly consists of four parts: the user's terminal, the server, the database, and the sentiment analysis engine.

[0747] 1. User's Device

[0748] The user's device is a smartphone or tablet, which has the ability to take pictures of food and drink and send them to the server via a dedicated app that is equipped with a camera function and a send button.

[0749] 2. Server

[0750] The server is responsible for image analysis, data acquisition, advice generation and notification functions.

[0751] Deep learning technologies such as TensorFlow are used for image analysis.

[0752] The Python NumPy library is used to match biological data with nutritional data and calculate nutritional balance.

[0753] For emotion recognition, we use the Google Speech-to-Text API and other tools to extract emotions from voice.

[0754] 3. Database

[0755] The database stores nutritional data for various foods and beverages, as well as biometric information for each user (age, gender, health status, medical history).

[0756] For example, obtain nutritional data such as protein and vitamin C for grilled chicken from a food database.

[0757] 4. Sentiment Analysis Engine

[0758] The emotion analysis engine has the function of recognizing the user's emotional state from their voice input and facial expression recognition, and providing that information to the server.

[0759] This allows the system to provide dietary advice based on the user's emotional state, such as stress or happiness.

[0760] Specific examples

[0761] Example 1: User A takes a photo of vegetable salad and grilled chicken for lunch. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies the vegetable salad and grilled chicken. The server then retrieves the nutritional information for each item from the food database and compares it with User A's biometric information to calculate the nutritional balance. Furthermore, the emotion analysis engine recognizes User A's emotional state and confirms that he or she is feeling stressed. The server generates advice recommending foods with a relaxing effect and sends it to the user via push notification.

[0762] Example 2: User B takes a photo of yogurt and fruit for breakfast. The photo sent to the server is analyzed and identified as yogurt and fruit. Nutritional information is then retrieved from the food database and matched with User B's biometric information. The sentiment analysis engine recognizes User B's happy emotional state and generates advice recommending foods to maintain that state, notifying the user via an in-app message.

[0763] This allows users to easily check the nutritional balance of their meals and receive dietary advice appropriate to their health and emotional state.

[0764] Prompt Sentence Examples

[0765] "You take a photo of your food or drink and send the image to our server. The system then analyzes the image and provides dietary advice based on your nutritional balance and emotional state."

[0766] The system allows users to better manage their health and make dietary choices that suit their emotional state.

[0767] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0768] Program processing flow

[0769] Step 1: Take and send a photo

[0770] Users take photos of food and drinks using their smartphones or tablets.

[0771] Specific operation: The user taps the "Take a photo" button in the dedicated app to launch the camera, takes a photo of the food or drink, and presses the "Send" button.

[0772] Input: A photo of food or drink taken by the user

[0773] Output: Food and drink images sent to the server

[0774] Step 2: Receiving and analyzing images

[0775] The server receives the image sent by the user and analyzes it using image analysis algorithms.

[0776] What it does: The server uses an image analysis library (e.g., TensorFlow) to identify food and drink objects in the image and determine their type and quantity.

[0777] Input: Food and drink images sent from the user's device

[0778] Output: Analyzed food and drink types and quantities

[0779] Step 3: Obtaining nutritional data

[0780] Based on the results of the image analysis, the server retrieves nutritional data for the identified food and beverage from a food database.

[0781] Specific operation: The server queries the food database to obtain nutritional information for food and beverage items (e.g., grilled chicken, vegetable salad, etc.).

[0782] Input: Type and quantity of food and drink analyzed

[0783] Output: Nutritional information

[0784] Step 4: Acquire biometric data

[0785] The server obtains the biometric data of pre-registered users, including age, gender, health status, and medical history.

[0786] Specific operation: The server retrieves the user's biometric information from the user database using an SQL query.

[0787] Input: User's ID

[0788] Output: User biometric data

[0789] Step 5: Calculate your nutritional balance

[0790] The server calculates the nutritional balance based on the acquired nutritional component data and biological information.

[0791] Specific operation: The server combines nutritional data and biological data and calculates nutritional balance using Python's NumPy library.

[0792] Input: Nutritional information and user biometric data

[0793] Output: Nutritional balance calculation results

[0794] Step 6: Obtaining emotion data

[0795] The emotion analysis engine recognizes the user's emotional state and sends the data to the server, using voice input, facial expressions, and biometric data.

[0796] How it works: Users record voice messages within the app, and the server passes the voice data to an emotion analysis engine to extract emotional states.

[0797] Input: User's voice or facial expression data

[0798] Output: Perceived emotional state of the user

[0799] Step 7: Generate dietary advice

[0800] The server generates optimal dietary advice for the user based on the nutritional balance calculation results and emotional state.

[0801] Specific operation: The server uses an advice generation algorithm to integrate nutritional balance and emotional state to generate customized advice.

[0802] Input: Nutritional balance calculation results and emotional state

[0803] Output: Optimal dietary advice

[0804] Step 8: Notifications and Display

[0805] The server sends the generated advice to the user's device, where it is delivered as a push notification or in-app message.

[0806] How it works: The server sends advice in JSON format to the user's device, which is then received by a dedicated app. The app then uses the notification API to generate a push notification, which the user can tap to display details.

[0807] Enter: Best Dietary Advice

[0808] Output: Advice displayed on the user's terminal

[0809] (Application example 2)

[0810] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0811] Managing employee health is a very important issue in manufacturing sites. Employees' emotional state is also an important factor, as it directly impacts work efficiency and safety. However, there is currently no way for employees to easily check the nutritional balance of their daily meals and receive appropriate dietary advice tailored to their emotional state. A system to improve this situation is needed.

[0812] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a terminal means for a user to take photos of food and beverages, a transmission means for sending the taken photos to the server, an image analysis means for analyzing the sent photos and identifying the type and amount of food and beverages, a nutritional data acquisition means for acquiring nutritional data of the identified food and beverages by referring to a food database, a healthcare data acquisition means for acquiring health care data of a pre-registered user, a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional data and health care data, an emotion analysis means for customizing dietary advice based on the acquired emotion data, an advice generation means for generating dietary advice for the user based on the calculation results and the emotion data, and a notification means for sending the generated advice to the user's terminal. This makes it possible to provide dietary advice optimized for the health and emotional state of employees, thereby improving work efficiency and safety in the factory.

[0813] A "user terminal" is a device that has the function of taking photos of food and drink and sending the images to a server.

[0814] "Transmission means" refers to a method or device that has the function of transmitting a photograph taken from a user's terminal to a server.

[0815] "Image analysis means" refers to an algorithm or device that has the function of analyzing the transmitted photograph and identifying the type and quantity of food and drink.

[0816] The "nutrition data acquisition means" refers to a method or device that acquires nutritional component data of a specified food or drink by referring to a food database.

[0817] The "healthcare data acquisition means" refers to a method or device for acquiring healthcare data of a user who has been registered in advance.

[0818] The "nutritional balance calculation means" refers to an algorithm or device that has the function of calculating nutritional balance based on the acquired nutritional component data and healthcare data.

[0819] The "emotion analysis means" is an algorithm or device that has the function of customizing dietary advice based on acquired emotion data.

[0820] The "advice generation means" refers to a method or device that generates dietary advice for a user based on the calculation results and emotion data.

[0821] The "notification means" is a method or device that has the function of transmitting the generated advice to the user's terminal.

[0822] This invention is implemented as a system for providing dietary advice based on the health management and emotional state of employees in a factory. The system mainly consists of a user terminal, a server, a database, and an emotion engine.

[0823] User's device

[0824] The user's device is a mobile device such as a smartphone that has the function of taking photos of the food and beverages served in the factory cafeteria. A dedicated application is also installed on this device, allowing the user to send the photos to the server.

[0825] server

[0826] The server has the following functions:

[0827] Image analysis means: Deep learning technology is used to analyze photos of food and drink sent from the user's device to identify the type and quantity of food and drink, and to obtain nutritional data for the identified food and drink from a food database.

[0828] Healthcare data acquisition means: Acquires healthcare data of pre-registered users from a database.

[0829] Emotion analysis means: The emotional state of the user is analyzed using an emotion engine based on data such as voice input and facial expressions obtained from the user's device.

[0830] Nutritional balance calculation means: Calculates the user's nutritional balance based on the acquired nutritional data and health care data.

[0831] Advice generation means: Based on the calculation results and the analyzed emotion data, dietary advice is generated for the user.

[0832] Notification method: The generated advice is sent to the user's device in the form of a push notification or similar.

[0833] Database

[0834] The database stores nutritional data for various foods and beverages, as well as healthcare data for each user. The food database consists of multiple lists, each containing detailed information such as the nutritional content and calories of each food.

[0835] Emotion Engine

[0836] The emotion engine uses a deep learning model to recognize the user's emotional state. The engine analyzes the user's emotional state based on their voice tone and facial expression data, and provides this information to the server.

[0837] Specific examples

[0838] For example, an employee takes a photo of the food served in the cafeteria at lunchtime with their smartphone and sends it to a server via a dedicated app. At this time, the user uses voice input to record their emotional state. The server analyzes the sent photo and voice data to identify the type and amount of food and drink and the user's emotional state. It then retrieves the necessary data from a food database and a healthcare database and calculates nutritional balance. It uses an emotion engine to analyze the user's emotional state and generates optimal dietary advice based on that. Finally, the generated advice is pushed to the user's device.

[0839] Prompt Sentence Examples

[0840] Image path: . / lunch.jpg

[0841] Path to the audio data: . / user_voice.wav

[0842] User ID: employee_12345

[0843] In this way, a system can be designed that allows employees to easily check the nutritional balance of their meals and receive dietary advice tailored to their health and emotional state, thereby improving the quality of their daily diet and increasing work efficiency and safety in the factory.

[0844] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0845] Step 1:

[0846] A user takes a photo of food or drink using a mobile device such as a smartphone. At this time, a dedicated application is launched and image data of the food or drink is acquired using the built-in camera. The input is the photo of the food or drink (image data), and the output is the image taken by the user.

[0847] Step 2:

[0848] The user sends the photographic data of the food or drink they have taken to the server via a dedicated application. The input is the image data they have just taken, and the output is the image data sent to the server.

[0849] Step 3:

[0850] The server receives the transmitted photo data and uses an image analysis algorithm to identify the types and quantities of food and drink contained in the photo. The input is the received image data, and the output is a list of identified food and drink items and their quantities. Specific operations include image recognition processing using a deep learning model.

[0851] Step 4:

[0852] The server references a food database to retrieve nutritional data related to the identified foods and beverages. The input is a list of the identified foods and beverages, and the output is the nutritional data corresponding to each food and beverage. Specific operations include retrieving data through a database query.

[0853] Step 5:

[0854] The server retrieves pre-registered user healthcare data from a database. The input is the user's ID information, and the output is the corresponding user's healthcare data. Specific operations include retrieving data through a database query.

[0855] Step 6:

[0856] The server calculates nutritional balance based on the acquired nutritional component data and healthcare data. The input is nutritional component data and healthcare data, and the output is the result of the nutritional balance calculation. Specific operations include comparing the required amount of nutrients with the amount of intake and calculating deficiencies and excesses.

[0857] Step 7:

[0858] Voice input and facial expression data are sent from the user's device, and the server receives the data and analyzes it using an emotion engine. The input is voice data and facial expression data, and the output is the user's emotional state. Specifically, deep learning models for voice recognition and facial expression analysis are used.

[0859] Step 8:

[0860] The server generates dietary advice for the user based on the nutritional balance calculation results and the analyzed emotion data. The input is the nutritional balance calculation results and emotion data, and the output is customized dietary advice. Specific operations include an algorithm that takes both data into account and proposes an optimal meal plan.

[0861] Step 9:

[0862] The server notifies the user's device of the generated advice. The input is the generated dietary advice, and the output is a notification message to the user's device. Specific operations include sending push notifications and in-app messages.

[0863] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0864] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0865] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0866] [Third embodiment]

[0867] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0868] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0869] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0870] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0871] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0872] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0873] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0874] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0875] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0876] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0877] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0878] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0879] This invention is a system in which a user takes a photo of food or drink and sends the image to a server, which analyzes the image to identify the type and amount of food or drink, calculates the nutritional balance, and provides appropriate dietary advice.

[0880] Overall system configuration

[0881] The system mainly consists of three parts: the user's terminal, the server, and the database.

[0882] Users' devices, such as smartphones or tablets, have the ability to take photos of food and drinks and send the images to a server via a dedicated app.

[0883] The server analyzes the images, identifies the type and amount of food and drink, obtains nutritional data, compares it with the user's health care data to calculate nutritional balance, and generates dietary advice.

[0884] The database stores nutritional data for various foods and beverages, as well as healthcare data for each user.

[0885] Details of each function

[0886] 1. Take and send a photo

[0887] Users can take photos of food and drink using their smartphones or tablets, and then use a dedicated app to send the photos to the server.

[0888] For example, a user takes a photo of a vegetable salad and grilled chicken for lunch and uploads it to the server via the app.

[0889] 2. Image analysis and calculation of nutritional balance

[0890] The server analyzes the image sent and uses image analysis algorithms to identify the type and quantity of food and drink.

[0891] For example, deep learning techniques can be used to identify vegetable salad and grilled chicken from images.

[0892] Next, the system retrieves nutritional data for the identified food and drink from a food database, such as the vitamin C content of a vegetable salad or the protein content of grilled chicken.

[0893] 3. Matching with healthcare data

[0894] The server obtains the user's pre-registered healthcare data, including the user's age, gender, health status, and medical history.

[0895] The system calculates nutritional balance based on nutritional component data and health care data. For example, if a user has an iron deficiency, that deficiency will be reflected in the analysis results.

[0896] 4. Generating and notifying dietary advice

[0897] Based on the calculation results, the server generates dietary advice for the user, such as "We recommend adding spinach to supplement iron."

[0898] The advice is sent to the user's device, and the user can review the advice within the app and incorporate it into their next meal plan.

[0899] Specific examples

[0900] Example 1:

[0901] User A takes a photo of vegetable salad and grilled chicken for lunch. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies the vegetable salad and grilled chicken. It then retrieves the nutritional information for each item from a food database and compares it with User A's healthcare data. If it determines that User A is iron deficient, the server generates advice and notifies the user to add spinach and liver.

[0902] Example 2:

[0903] User B takes a photo of yogurt and fruit for breakfast. When the photo is sent to the server, the server analyzes the image and identifies the yogurt and fruit. The server then retrieves their nutritional information from a food database and compares it with User B's healthcare data to calculate nutritional balance. If the server determines that User B is calcium deficient, it generates and notifies User B with advice to add cheese or milk to supplement calcium.

[0904] In this way, the system is designed to allow users to easily check the nutritional balance of their meals and receive dietary advice tailored to their health condition, thereby improving the quality of their daily diet and preventing illness.

[0905] The processing flow will be explained below.

[0906] Program processing flow and specific operations

[0907] System-wide processing steps

[0908] Step 1:

[0909] Users use their devices to take photos of food and beverages, and it is important to use the camera app on their smartphone or tablet to capture clear images.

[0910] Step 2:

[0911] The user launches a dedicated app and takes a photo, sends it to the server, and the app packages the photo data as an HTTP request and uploads it to the server.

[0912] Step 3:

[0913] The server receives the uploaded photo data and temporarily stores it in a database, along with metadata (such as the time of submission, user ID, etc.).

[0914] Step 4:

[0915] The server runs an image analysis algorithm to analyze the stored photo data, which uses a deep learning model (e.g., CNN) to identify the type and quantity of food and drink.

[0916] Step 5:

[0917] The server retrieves nutritional information from the food database based on the identified food and drink data, for example, by querying the vitamin C content of a specified vegetable salad or the protein content of grilled chicken.

[0918] Step 6:

[0919] The server retrieves the user's healthcare data, including the user's age, gender, health status, and medical history, and uses a database query to retrieve pre-registered information.

[0920] Step 7:

[0921] The server calculates nutritional balance based on the acquired nutritional data and health care data. For example, if the user has an iron deficiency, the nutrition calculation will include that deficiency.

[0922] Step 8:

[0923] The server generates dietary advice for the user based on the calculation results, such as "We recommend adding spinach to supplement iron."

[0924] Step 9:

[0925] The server sends the generated advice to the user's device, where it is delivered as a push notification or in-app message.

[0926] Step 10:

[0927] Users can receive advice notifications on their device, open the app to view details, and take action to incorporate the advice into their next meal plan.

[0928] Through these steps, the system analyzes the user's diet and provides nutritional advice based on individual healthcare data to support a healthy diet.

[0929] Example 1

[0930] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0931] In today's busy lifestyles, many people face the challenge of properly managing their diet and nutritional balance. In particular, those who are deficient in certain nutrients or have a history of such deficiencies require appropriate nutritional management to improve their health. However, manually calculating nutritional information and generating dietary advice is cumbersome and requires specialized knowledge. Therefore, there is a need for a system that allows users to easily and efficiently manage their diet and receive appropriate dietary advice.

[0932] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0933] In this invention, the server includes an image acquisition means, a data transmission means, an image analysis means, an information acquisition means, a data acquisition means, a nutritional balance calculation means, a guidance generation means, and a data notification means. Based on image data of food and drink photographed by a user, the server automatically identifies the type and quantity of food and drink, acquires nutritional information, calculates nutritional balance based on the user's health management data, and generates and notifies the user of appropriate dietary guidance. This allows users to efficiently manage their eating habits and receive dietary guidance tailored to their health condition.

[0934] 1. "User" means an individual who uses the system to take photos of their own food and drink and receive dietary advice.

[0935] 2. "Terminal" means a smartphone, tablet, or other device with an image capture function that transmits image data captured by the user to the server.

[0936] 3. "Image acquisition means" refers to the functions and applications that allow users to take images of food and beverages using a terminal.

[0937] 4. "Data transmission means" refers to the communication protocol and functions for transmitting image data captured by the terminal to the server.

[0938] 5. "Server" means a computer system that analyzes received image data, identifies the type and quantity of food and beverages, calculates nutritional balance, and generates dietary advice.

[0939] 6. "Image analysis means" means software and algorithms that analyze image data within the server and identify the type and quantity of food and beverages.

[0940] 7. "Food Information Database" refers to a database that records nutritional information for various foods and beverages.

[0941] 8. "Information acquisition means" refers to the function or process of acquiring nutritional information of the food or beverage identified by the image analysis means from the food information database.

[0942] 9. "Health Management Data" means information about a user's health, such as the user's age, gender, health status, and medical history.

[0943] 10. "Data Capture Method" means a function or process for capturing pre-recorded user health management data.

[0944] 11. "Nutritional balance calculation means" refers to software or algorithms for calculating a user's nutritional balance based on acquired nutritional information and health management data.

[0945] 12. "Guidance generation means" refers to the functions and processes for generating specific dietary advice for the user based on the calculation results of the nutritional balance calculation means.

[0946] 13. "Data notification means" refers to the communication protocols and functions for sending the generated dietary advice to the user's terminal and notifying the user.

[0947] This invention is a system in which a user takes an image of food and drink and sends the image to a server, which analyzes the image to identify the type and quantity of food and drink, calculates the nutritional balance, and provides appropriate dietary advice.

[0948] The entire system is mainly composed of a user terminal, a server, a food information database, and a health management database. Specifically, the roles and processes of each component are as follows:

[0949] User's device

[0950] The user's device is a device such as a smartphone or tablet. A dedicated app is installed on this device and has the following functions:

[0951] 1. Image acquisition method: Users use this dedicated app to take images of food and drink.

[0952] 2. Data transmission means: The captured image data is transmitted to the server using the HTTPS protocol.

[0953] server

[0954] The server analyzes the received image data using the following various means and provides dietary advice to the user.

[0955] 1. Image analysis means: The server analyzes the received image data using a deep learning algorithm (e.g., TensorFlow or PyTorch) to identify the type and quantity of food and beverages.

[0956] 2. Information acquisition means: The server refers to the food information database and acquires nutritional information of the identified food or drink.

[0957] 3. Data acquisition means: The server acquires the user's health management data, including age, gender, health status, and medical history, from the database.

[0958] 4. Nutritional balance calculation means: The server calculates nutritional balance based on the acquired nutritional component information and health management data.

[0959] 5. Guidance generation means: Based on the calculation results, the server generates specific dietary advice for the user.

[0960] 6. Data notification method: The generated dietary advice is sent to the user's device via a dedicated app.

[0961] Database

[0962] 1. Food information database: A database that records nutritional information for various foods and beverages.

[0963] 2. Health management database: A database that records information about the user's health.

[0964] Specific examples

[0965] 1. Example 1:

[0966] User A takes image data of vegetable salad and grilled chicken for lunch.

[0967] Image data is sent to the server using a dedicated app.

[0968] The server analyzes the image and identifies the vegetable salad and grilled chicken. It then retrieves the nutritional information for each from a food information database and compares it with User A's health management data.

[0969] The server confirms that User A is iron deficient, generates dietary advice such as "We recommend adding spinach or liver to supplement your iron intake," and notifies the user's device.

[0970] 2. Example 2:

[0971] User B takes an image of yogurt and fruit for breakfast.

[0972] Image data is sent to the server using a dedicated app.

[0973] The server analyzes the image, identifies the yogurt and fruit, retrieves their nutritional information from a food information database, and compares it with User B's health management data to calculate the nutritional balance.

[0974] The server confirms that User B is calcium deficient, generates dietary advice such as "We recommend adding cheese or milk to supplement calcium," and notifies the user's device.

[0975] Example prompts (for generative AI models)

[0976] "I took a photo of my lunch and uploaded it. The analysis showed that it was a vegetable salad and grilled chicken. Please tell me the detailed nutritional balance."

[0977] "I sent a photo of my breakfast: yogurt and fruit. Please provide dietary advice that takes my health condition into consideration."

[0978] This system allows users to easily manage their eating habits and receive dietary advice based on their health condition, thereby improving the quality of their daily eating habits and achieving proper nutritional management.

[0979] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0980] Step 1:

[0981] Users take images of food and drink using a dedicated app on their smartphone or tablet. The input is image data of the food and drink, and the output is the image data. Specifically, the user takes a photo of their lunch and saves the image data in JPEG format.

[0982] Step 2:

[0983] The device sends the captured image data to the server using the HTTPS protocol. The input is the captured image data, and the output is the transmission of the image data to the server. For example, the device uploads the captured image to the server via a dedicated app.

[0984] Step 3:

[0985] The server analyzes the received image data. Using a deep learning model (e.g., TensorFlow or PyTorch), it identifies the type and quantity of food and drink from the image. The input is the sent image data, and the output is the type and quantity of identified food and drink. Specifically, it recognizes vegetable salad and grilled chicken through image analysis.

[0986] Step 4:

[0987] The server references the food information database to obtain nutritional information for the identified food or drink. The input is the type of food or drink identified, and the output is the obtained nutritional information. For example, the vitamin C content of a vegetable salad or the protein content of grilled chicken is obtained.

[0988] Step 5:

[0989] The server refers to the health management database to obtain the user's health management data. The input is the user's identification information, and the output is the user's health management data. For example, the server obtains information about User A's age, gender, and iron deficiency.

[0990] Step 6:

[0991] The server calculates nutritional balance based on the acquired nutritional information and health management data. The input is nutritional information and health management data, and the output is the calculated nutritional balance. For example, taking into account User A's iron deficiency, the server comprehensively evaluates the nutritional data of the vegetable salad and grilled chicken they ate.

[0992] Step 7:

[0993] The server generates specific dietary advice for the user based on the calculation results. The input is the calculation result of nutritional balance, and the output is the generated dietary advice. Specifically, it generates advice such as "We recommend adding spinach or liver to supplement iron."

[0994] Step 8:

[0995] The device receives the dietary advice sent from the server and displays it in the dedicated app. The input is the dietary advice from the server, and the output is what is displayed to the user. The user receives a push notification and can check the specific dietary advice in the dedicated app.

[0996] (Application example 1)

[0997] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0998] Many people today suffer from poor eating habits and a lack of nutritional balance, making nutritional management difficult, especially when eating out frequently at cafes and restaurants. Furthermore, there is a lack of systems that allow users to receive accurate dietary advice tailored to their individual health conditions. It is necessary to provide a support system that solves this problem and enables users to choose healthy meals even when eating out.

[0999] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1000] In this invention, the server includes a terminal means for a user to take photos of food and beverages, a transmission means for transmitting the photographed photos to the server, an image analysis means in the server for analyzing the transmitted photos and identifying the type and amount of food and beverages, a nutritional data acquisition means for acquiring nutritional data of the identified food and beverages by referring to a food database, a healthcare data acquisition means for acquiring health care data of the user registered in advance, a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional data and the healthcare data, an advice generation means for generating dietary advice for the user based on the calculation results, a notification means for transmitting the generated advice to the user's terminal, and a supplemental food suggestion means for analyzing photos of food and beverages taken by the user, retrieving related supplemental nutritional information from an external database based on the acquired nutritional data, and suggesting foods that optimize nutritional balance. This allows users to receive dietary advice suited to their own health condition even when eating out, helping them to achieve a nutritionally balanced diet.

[1001] "Terminal means" refers to a device that a user uses to take a photo of food or drink and send the photo to the server. Specifically, it refers to a mobile information terminal such as a smartphone or tablet.

[1002] "Transmission means" refers to the function or process for sending photos taken by the user to the server, including internet communication functions and send buttons within the application.

[1003] "Image analysis means" refers to the technology or algorithms used to analyze the submitted photograph and identify the type and quantity of food or drink, including image recognition technology using deep learning.

[1004] "Nutrition data acquisition means" refers to a function that acquires nutritional component data of the identified food or drink by referring to a food database.

[1005] "Means for obtaining health care data" refers to the function of obtaining the user's pre-registered health care data (age, gender, health condition, medical history, etc.).

[1006] "Nutritional balance calculation means" refers to the technology or algorithm for calculating nutritional balance based on acquired nutritional component data and healthcare data.

[1007] "Advice generation means" refers to a function that generates dietary advice for the user based on the results of nutritional balance calculations.

[1008] "Notification means" refers to a function for sending the generated advice to the user's terminal and notifying the user.

[1009] "Supplementary food suggestion means" refers to a function that analyzes photos of food and beverages taken by the user, obtains related supplementary nutritional information from an external database based on the acquired nutritional data, and suggests foods that will optimize nutritional balance.

[1010] The system that realizes this application example allows the user to take a photo of food or drink and send the image to a server, which then analyzes the image to identify the type and amount of food or drink, calculates the nutritional balance, and provides appropriate dietary advice.

[1011] System configuration

[1012] The system consists of three main parts: the user's terminal, the server, and the database.

[1013] 1. User's Device

[1014] Users take photos of food and drink using a mobile information terminal such as a smartphone and send the photos to the server via a dedicated application, which then uses its internet communication function to send the photo data to the server.

[1015] 2. Server

[1016] Image analysis methods

[1017] The server receives the image and analyzes it using deep learning techniques, including libraries such as TensorFlow and Keras, to identify the type and quantity of food and drink.

[1018] Nutritional data acquisition method

[1019] The server references a food database based on the identified food and drink to obtain nutritional information, which may also be obtained from an external nutrition information API (e.g., "nutrition-api.com").

[1020] Healthcare data acquisition method

[1021] The server acquires the user's pre-registered health care data (age, gender, health condition, medical history, etc.), which is stored in a database.

[1022] Nutritional Balance Calculator

[1023] The server calculates nutritional balance based on the acquired nutritional data and healthcare data, using an algorithm created in Python or other programs.

[1024] Advice Generation Method

[1025] The server generates dietary advice based on the calculation results. The advice generated suggests nutritional supplements tailored to the user's health condition. For example, if the user is iron deficient, it will recommend adding spinach and liver.

[1026] Notification means

[1027] The server then sends the generated advice to the user's device via a dedicated application.

[1028] Supplementary food suggestion means

[1029] The server analyzes photos of food and drink taken by the user, retrieves relevant supplemental nutritional information from an external database based on the acquired nutritional data, and suggests foods to optimize nutritional balance, taking into account the user's health care data.

[1030] Specific examples

[1031] Specific examples are shown below.

[1032] 1. Example 1: A user takes a photo of "chicken salad" at a cafe. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies it as "chicken salad." The server then retrieves the nutritional data for "chicken salad" from the nutrition information API and identifies the user's nutritional deficiency (e.g., iron deficiency). The server generates advice recommending "add spinach" to supplement iron and notifies the user's device.

[1033] Prompt Sentence Examples

[1034] "Please analyze photos of food and drink provided by the user and identify the type of food and drink. Retrieve nutritional information for the identified food and drink from an external API, and match it with the user's health data to suggest specific foods to supplement any nutrient deficiencies."

[1035] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1036] Step 1:

[1037] The user takes a photo of the food or drink.

[1038] Input: Image data captured by a user using a mobile information device such as a smartphone.

[1039] Output: Captured image data.

[1040] Specific actions: A user takes a photo of a "chicken salad" at a cafe. Using the camera application on their smartphone, they take a photo that captures the entire food item.

[1041] Step 2:

[1042] The captured photo is sent to the server.

[1043] Input: Captured image data.

[1044] Output: Image data sent to the server.

[1045] Specific operation: The user opens the dedicated application and presses the "send button" to send the photo they have taken to the server. The application then uploads the photo to the server via the Internet.

[1046] Step 3:

[1047] The server analyzes the photos sent and identifies the type and quantity of food and drink.

[1048] Input: Image data sent to the server.

[1049] Output: Data on the type and quantity of food and drink consumed.

[1050] How it works: The server analyzes the received image data using a deep learning model (e.g., using TensorFlow or Keras). The model identifies the type of food in the image (e.g., "chicken salad") and estimates its quantity.

[1051] Step 4:

[1052] The server refers to the food database to obtain nutritional component data of the identified food or drink.

[1053] Input: Data about the type of food or drink.

[1054] Output: Nutritional information for food and drink.

[1055] Specific behavior: The server requests and obtains the nutritional information for the specified food or drink from a food database or an external nutrition information API (e.g., "nutrition-api.com"). For example, it obtains the calorie and macronutrient data for "chicken salad."

[1056] Step 5:

[1057] The server acquires healthcare data of pre-registered users.

[1058] Input: User's identity information.

[1059] Output: User's healthcare data.

[1060] Specific operation: The server accesses the database and retrieves healthcare data (age, gender, health status, medical history, etc.) based on the user's ID. For example, the healthcare data may confirm that the user is iron deficient.

[1061] Step 6:

[1062] The server calculates nutritional balance based on the acquired nutritional component data and health care data.

[1063] Input: Nutritional information of food and drink and user's healthcare data.

[1064] Output: Nutritional balance data.

[1065] How it works: The server uses programs such as Python to compare the nutritional information of food and drink with the user's health data and calculate nutrient deficiencies or excesses. For example, it calculates the iron deficiency that occurs when eating chicken salad alone.

[1066] Step 7:

[1067] The server generates dietary advice for the user based on the calculation results.

[1068] Input: Nutritional balance data.

[1069] Output: Dietary advice.

[1070] Specific action: The server generates advice suggesting specific foods to supplement the missing nutrients, for example, recommending "add spinach to supplement iron."

[1071] Step 8:

[1072] The server sends the generated advice to the user's terminal for notification.

[1073] Enter: dietary advice.

[1074] Output: An advisory notification that is displayed on the user's device.

[1075] How it works: The server sends advice to the user's smartphone via a dedicated application, and the user can then check the advice within the app.

[1076] Step 9:

[1077] The server analyzes photos of food and drink taken by the user, retrieves relevant supplemental nutritional information from an external database based on the acquired nutritional data, and suggests foods that will optimize nutritional balance.

[1078] Input: Food and beverage type data and nutritional information.

[1079] Output: Food suggestion data to optimize nutritional balance.

[1080] Specific operation: Based on the analysis results, the server retrieves food information corresponding to the missing nutrients from an external database (e.g., "nutrition-api.com") and suggests supplementary foods (e.g., "spinach" to supplement iron).

[1081] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1082] This invention combines a system that takes photos of food and drink, analyzes the transmitted images to calculate nutritional balance, and provides personalized dietary advice with an emotion engine that recognizes the user's emotions and customizes dietary advice based on their emotional state.

[1083] Overall system configuration

[1084] The system mainly consists of four parts: the user's terminal, the server, the database, and the emotion engine.

[1085] Users' devices, such as smartphones or tablets, have the ability to take photos of food and drinks and send the images to a server via a dedicated app.

[1086] The server analyzes the images, identifies the type and amount of food and drink, obtains nutritional data, compares it with the user's health care data and emotional state to calculate nutritional balance and generate dietary advice.

[1087] The database stores nutritional data for various foods and beverages, as well as healthcare data for each user.

[1088] The emotion engine has the function of analyzing the user's emotional state and providing that information to the server.

[1089] Details of each function

[1090] 1. Take and send a photo

[1091] Users can take photos of food and drink using their smartphones or tablets, and then use a dedicated app to send the photos to the server.

[1092] For example, a user takes a photo of a vegetable salad and grilled chicken for lunch and uploads it to the server via the app.

[1093] 2. Image analysis and calculation of nutritional balance

[1094] The server analyzes the image sent and uses image analysis algorithms to identify the type and quantity of food and drink.

[1095] For example, deep learning techniques can be used to identify vegetable salad and grilled chicken from images.

[1096] 3. Obtaining nutritional data

[1097] The server retrieves nutritional data for the identified food or drink from a food database, such as the vitamin C content of a vegetable salad or the protein content of grilled chicken.

[1098] 4. Healthcare data acquisition and collation

[1099] The server obtains the user's pre-registered healthcare data, including the user's age, gender, health status, and medical history.

[1100] Nutritional balance is calculated based on nutritional component data and health care data. For example, if a user has an iron deficiency, the nutritional calculation will include that deficiency.

[1101] 5. Acquiring and matching emotion data

[1102] The emotion engine recognizes the user's emotional state and sends that information to the server using the user's voice input, facial expressions, or other biometric data.

[1103] For example, voice recognition technology can be used to analyze the tone and pace of a user's voice to identify their emotional state.

[1104] 6. Generating and customizing dietary advice

[1105] The server generates optimal dietary advice for the user based on the nutritional balance calculation results and emotional state.

[1106] For example, if a user is feeling stressed, the system generates advice recommending foods that have a relaxing effect.

[1107] 7. Notices and Displays

[1108] The server sends the generated advice to the user's device, where it is delivered as a push notification or in-app message.

[1109] Users can receive advice notified on their device, open the app to check the details, and incorporate them into their next meal plan.

[1110] Specific examples

[1111] Example 1:

[1112] User A takes a photo of vegetable salad and grilled chicken for lunch. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies the vegetable salad and grilled chicken. It then retrieves the nutritional information for each item from a food database and compares it with User A's healthcare data. The emotion engine then recognizes User A's emotional state and sends it to the server. The server, having determined that User A is feeling stressed, generates and notifies User A of advice recommending foods that have a relaxing effect.

[1113] Example 2:

[1114] User B takes a photo of yogurt and fruit for breakfast. When the photo is sent to the server, the server analyzes the image and identifies the yogurt and fruit. It then retrieves their nutritional information from a food database and compares it with User B's healthcare data to calculate nutritional balance. The emotion engine then recognizes User B's emotional state and sends it to the server. The server, having confirmed that User B is feeling happy, generates and notifies User B of advice recommending foods to help maintain that mood.

[1115] The system is designed to allow users to easily check the nutritional balance of their meals and receive dietary advice tailored to their health and emotional state, helping to improve the quality of their daily diet and prevent illness.

[1116] The processing flow will be explained below.

[1117] Program processing flow and specific operations

[1118] System-wide processing steps

[1119] Step 1:

[1120] The user uses the device to take a photo of the food or drink. The user launches the camera app on their smartphone or tablet and takes a photo of the food or drink.

[1121] Step 2:

[1122] The user launches the dedicated app and sends the photos they have taken to the server, which then uploads the photo data to the server in the form of an HTTP request.

[1123] Step 3:

[1124] The server receives the uploaded photo data and temporarily stores it in a database, while also recording metadata such as the time of submission and user ID.

[1125] Step 4:

[1126] The server runs an image analysis algorithm, using a deep learning model (e.g., CNN) to identify the type and quantity of food and drink. The identified type and quantity are then stored in a database.

[1127] Step 5:

[1128] The server queries a food database to obtain nutritional information for the identified food or drink, such as the vitamin C content of a vegetable salad or the protein content of grilled chicken.

[1129] Step 6:

[1130] The server retrieves the healthcare data of pre-registered users, using a database query to obtain a dataset containing the user's age, gender, health status, and medical history.

[1131] Step 7:

[1132] The server calculates nutritional balance based on the acquired nutritional data and health care data. For example, if the user has an iron deficiency, the nutrition calculation will take that deficiency into account.

[1133] Step 8:

[1134] The emotion engine recognizes the user's emotional state. Emotion recognition algorithms are run to analyze the user's voice input, facial expressions, or other biometric data.

[1135] Step 9:

[1136] The emotion engine sends the user's recognized emotion data to the server, along with metadata about the user's emotional state.

[1137] Step 10:

[1138] The server combines the nutritional balance calculation results with data from the emotion engine. Taking into account the user's emotional state, it generates appropriate dietary advice. For example, it recommends foods with a relaxing effect to a user who is feeling stressed.

[1139] Step 11:

[1140] The server then sends the generated dietary advice to the user's device in the form of a push notification or an in-app message.

[1141] Step 12:

[1142] Users can receive advice notifications on their device, open the app to view details, and use the advice to plan their next meal.

[1143] Through these steps, the system analyzes the user's diet and provides customized nutritional advice based on individual healthcare and emotional data, supporting a healthy diet.

[1144] Example 2

[1145] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1146] Conventional dietary advice systems provide advice based only on the nutritional balance of foods and beverages, making it difficult to provide detailed dietary advice that reflects the user's emotional state. Therefore, there is a need for a method that generates optimal dietary advice that reflects the user's emotional state and stress level, thereby improving the user's overall health.

[1147] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1148] In this invention, the server includes emotion analysis means for analyzing the emotional state of the user, nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional component data and biological information, and advice generation means for generating dietary advice for the user based on the nutritional balance calculation result and the emotional state, thereby making it possible to provide more precise and personalized dietary advice that also takes the emotional state of the user into consideration.

[1149] "User" refers to a person using this system.

[1150] "Terminal means" refers to an electronic device that allows a user to take pictures of food and drink and send them to the server.

[1151] "Photographing means" refers to a function that allows a user to photograph an image using a terminal means.

[1152] "Transmission means" refers to a function for transmitting captured images to a server.

[1153] "Server" refers to the computer system that analyzes images sent by users, acquires and processes various data, and generates dietary advice.

[1154] "Image analysis means" refers to a function that analyzes the transmitted image and identifies the type and amount of food and drink.

[1155] "Food database" refers to a database that stores nutritional information for various foods and beverages.

[1156] "Nutrition data acquisition means" refers to a function for acquiring nutritional component data of a specified food or drink from a food database.

[1157] "Biometric data acquisition means" refers to a function that acquires biometric information such as the age, gender, health condition, and medical history of a pre-registered user.

[1158] "Nutritional balance calculation means" refers to a function that calculates nutritional balance based on the acquired nutritional component data and the user's biological information.

[1159] "Emotion analysis means" refers to a function that recognizes the user's emotional state and provides that data to the server.

[1160] "Advice generation means" refers to a function that generates optimal dietary advice for a user based on the nutritional balance calculation results and emotional state.

[1161] "Notification means" refers to a function that sends the generated advice to the user's terminal.

[1162] MODE FOR CARRYING OUT THE INVENTION

[1163] This invention is a system that allows users to take photos of food and drink and send them to a server, which uses image analysis algorithms to calculate nutritional balance and provide personalized dietary advice. Furthermore, it can recognize the user's emotional state and use that information to better customize dietary advice.

[1164] Overall system configuration

[1165] The system mainly consists of four parts: the user's terminal, the server, the database, and the sentiment analysis engine.

[1166] 1. User's Device

[1167] The user's device is a smartphone or tablet, which has the ability to take pictures of food and drink and send them to the server via a dedicated app that is equipped with a camera function and a send button.

[1168] 2. Server

[1169] The server is responsible for image analysis, data acquisition, advice generation and notification functions.

[1170] Deep learning technologies such as TensorFlow are used for image analysis.

[1171] The Python NumPy library is used to match biological data with nutritional data and calculate nutritional balance.

[1172] For emotion recognition, we use the Google Speech-to-Text API and other tools to extract emotions from voice.

[1173] 3. Database

[1174] The database stores nutritional data for various foods and beverages, as well as biometric information for each user (age, gender, health status, medical history).

[1175] For example, obtain nutritional data such as protein and vitamin C for grilled chicken from a food database.

[1176] 4. Sentiment Analysis Engine

[1177] The emotion analysis engine has the function of recognizing the user's emotional state from their voice input and facial expression recognition, and providing that information to the server.

[1178] This allows the system to provide dietary advice based on the user's emotional state, such as stress or happiness.

[1179] Specific examples

[1180] Example 1: User A takes a photo of vegetable salad and grilled chicken for lunch. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies the vegetable salad and grilled chicken. The server then retrieves the nutritional information for each item from the food database and compares it with User A's biometric information to calculate the nutritional balance. Furthermore, the emotion analysis engine recognizes User A's emotional state and confirms that he or she is feeling stressed. The server generates advice recommending foods with a relaxing effect and sends it to the user via push notification.

[1181] Example 2: User B takes a photo of yogurt and fruit for breakfast. The photo sent to the server is analyzed and identified as yogurt and fruit. Nutritional information is then retrieved from the food database and matched with User B's biometric information. The sentiment analysis engine recognizes User B's happy emotional state and generates advice recommending foods to maintain that state, notifying the user via an in-app message.

[1182] This allows users to easily check the nutritional balance of their meals and receive dietary advice appropriate to their health and emotional state.

[1183] Prompt Sentence Examples

[1184] "You take a photo of your food or drink and send the image to our server. The system then analyzes the image and provides dietary advice based on your nutritional balance and emotional state."

[1185] The system allows users to better manage their health and make dietary choices that suit their emotional state.

[1186] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1187] Program processing flow

[1188] Step 1: Take and send a photo

[1189] Users take photos of food and drinks using their smartphones or tablets.

[1190] Specific operation: The user taps the "Take a photo" button in the dedicated app to launch the camera, takes a photo of the food or drink, and presses the "Send" button.

[1191] Input: A photo of food or drink taken by the user

[1192] Output: Food and drink images sent to the server

[1193] Step 2: Receiving and analyzing images

[1194] The server receives the image sent by the user and analyzes it using image analysis algorithms.

[1195] What it does: The server uses an image analysis library (e.g., TensorFlow) to identify food and drink objects in the image and determine their type and quantity.

[1196] Input: Food and drink images sent from the user's device

[1197] Output: Analyzed food and drink types and quantities

[1198] Step 3: Obtaining nutritional data

[1199] Based on the results of the image analysis, the server retrieves nutritional data for the identified food and beverage from a food database.

[1200] Specific operation: The server queries the food database to obtain nutritional information for food and beverage items (e.g., grilled chicken, vegetable salad, etc.).

[1201] Input: Type and quantity of food and drink analyzed

[1202] Output: Nutritional information

[1203] Step 4: Acquire biometric data

[1204] The server obtains the biometric data of pre-registered users, including age, gender, health status, and medical history.

[1205] Specific operation: The server retrieves the user's biometric information from the user database using an SQL query.

[1206] Input: User's ID

[1207] Output: User biometric data

[1208] Step 5: Calculate your nutritional balance

[1209] The server calculates the nutritional balance based on the acquired nutritional component data and biological information.

[1210] Specific operation: The server combines nutritional data and biological data and calculates nutritional balance using Python's NumPy library.

[1211] Input: Nutritional information and user biometric data

[1212] Output: Nutritional balance calculation results

[1213] Step 6: Obtaining emotion data

[1214] The emotion analysis engine recognizes the user's emotional state and sends the data to the server, using voice input, facial expressions, and biometric data.

[1215] How it works: Users record voice messages within the app, and the server passes the voice data to an emotion analysis engine to extract emotional states.

[1216] Input: User's voice or facial expression data

[1217] Output: Perceived emotional state of the user

[1218] Step 7: Generate dietary advice

[1219] The server generates optimal dietary advice for the user based on the nutritional balance calculation results and emotional state.

[1220] Specific operation: The server uses an advice generation algorithm to integrate nutritional balance and emotional state to generate customized advice.

[1221] Input: Nutritional balance calculation results and emotional state

[1222] Output: Optimal dietary advice

[1223] Step 8: Notifications and Display

[1224] The server sends the generated advice to the user's device, where it is delivered as a push notification or in-app message.

[1225] How it works: The server sends advice in JSON format to the user's device, which is then received by a dedicated app. The app then uses the notification API to generate a push notification, which the user can tap to display details.

[1226] Enter: Best Dietary Advice

[1227] Output: Advice displayed on the user's terminal

[1228] (Application example 2)

[1229] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1230] Managing employee health is a very important issue in manufacturing sites. Employees' emotional state is also an important factor, as it directly impacts work efficiency and safety. However, there is currently no way for employees to easily check the nutritional balance of their daily meals and receive appropriate dietary advice tailored to their emotional state. A system to improve this situation is needed.

[1231] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a terminal means for a user to take photos of food and beverages, a transmission means for sending the taken photos to the server, an image analysis means for analyzing the sent photos and identifying the type and amount of food and beverages, a nutritional data acquisition means for acquiring nutritional data of the identified food and beverages by referring to a food database, a healthcare data acquisition means for acquiring health care data of a pre-registered user, a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional data and health care data, an emotion analysis means for customizing dietary advice based on the acquired emotion data, an advice generation means for generating dietary advice for the user based on the calculation results and the emotion data, and a notification means for sending the generated advice to the user's terminal. This makes it possible to provide dietary advice optimized for the health and emotional state of employees, thereby improving work efficiency and safety in the factory.

[1232] A "user terminal" is a device that has the function of taking photos of food and drink and sending the images to a server.

[1233] "Transmission means" refers to a method or device that has the function of transmitting a photograph taken from a user's terminal to a server.

[1234] "Image analysis means" refers to an algorithm or device that has the function of analyzing the transmitted photograph and identifying the type and quantity of food and drink.

[1235] The "nutrition data acquisition means" refers to a method or device that acquires nutritional component data of a specified food or drink by referring to a food database.

[1236] The "healthcare data acquisition means" refers to a method or device for acquiring healthcare data of a user who has been registered in advance.

[1237] The "nutritional balance calculation means" refers to an algorithm or device that has the function of calculating nutritional balance based on the acquired nutritional component data and healthcare data.

[1238] The "emotion analysis means" is an algorithm or device that has the function of customizing dietary advice based on acquired emotion data.

[1239] The "advice generation means" refers to a method or device that generates dietary advice for a user based on the calculation results and emotion data.

[1240] The "notification means" is a method or device that has the function of transmitting the generated advice to the user's terminal.

[1241] This invention is implemented as a system for providing dietary advice based on the health management and emotional state of employees in a factory. The system mainly consists of a user terminal, a server, a database, and an emotion engine.

[1242] User's device

[1243] The user's device is a mobile device such as a smartphone that has the function of taking photos of the food and beverages served in the factory cafeteria. A dedicated application is also installed on this device, allowing the user to send the photos to the server.

[1244] server

[1245] The server has the following functions:

[1246] Image analysis means: Deep learning technology is used to analyze photos of food and drink sent from the user's device to identify the type and quantity of food and drink, and to obtain nutritional data for the identified food and drink from a food database.

[1247] Healthcare data acquisition means: Acquires healthcare data of pre-registered users from a database.

[1248] Emotion analysis means: The emotional state of the user is analyzed using an emotion engine based on data such as voice input and facial expressions obtained from the user's device.

[1249] Nutritional balance calculation means: Calculates the user's nutritional balance based on the acquired nutritional data and health care data.

[1250] Advice generation means: Based on the calculation results and the analyzed emotion data, dietary advice is generated for the user.

[1251] Notification method: The generated advice is sent to the user's device in the form of a push notification or similar.

[1252] Database

[1253] The database stores nutritional data for various foods and beverages, as well as healthcare data for each user. The food database consists of multiple lists, each containing detailed information such as the nutritional content and calories of each food.

[1254] Emotion Engine

[1255] The emotion engine uses a deep learning model to recognize the user's emotional state. The engine analyzes the user's emotional state based on their voice tone and facial expression data, and provides this information to the server.

[1256] Specific examples

[1257] For example, an employee takes a photo of the food served in the cafeteria at lunchtime with their smartphone and sends it to a server via a dedicated app. At this time, the user uses voice input to record their emotional state. The server analyzes the sent photo and voice data to identify the type and amount of food and drink and the user's emotional state. It then retrieves the necessary data from a food database and a healthcare database and calculates nutritional balance. It uses an emotion engine to analyze the user's emotional state and generates optimal dietary advice based on that. Finally, the generated advice is pushed to the user's device.

[1258] Prompt Sentence Examples

[1259] Image path: . / lunch.jpg

[1260] Path to the audio data: . / user_voice.wav

[1261] User ID: employee_12345

[1262] In this way, a system can be designed that allows employees to easily check the nutritional balance of their meals and receive dietary advice tailored to their health and emotional state, thereby improving the quality of their daily diet and increasing work efficiency and safety in the factory.

[1263] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1264] Step 1:

[1265] A user takes a photo of food or drink using a mobile device such as a smartphone. At this time, a dedicated application is launched and image data of the food or drink is acquired using the built-in camera. The input is the photo of the food or drink (image data), and the output is the image taken by the user.

[1266] Step 2:

[1267] The user sends the photographic data of the food or drink they have taken to the server via a dedicated application. The input is the image data they have just taken, and the output is the image data sent to the server.

[1268] Step 3:

[1269] The server receives the transmitted photo data and uses an image analysis algorithm to identify the types and quantities of food and drink contained in the photo. The input is the received image data, and the output is a list of identified food and drink items and their quantities. Specific operations include image recognition processing using a deep learning model.

[1270] Step 4:

[1271] The server references a food database to retrieve nutritional data related to the identified foods and beverages. The input is a list of the identified foods and beverages, and the output is the nutritional data corresponding to each food and beverage. Specific operations include retrieving data through a database query.

[1272] Step 5:

[1273] The server retrieves pre-registered user healthcare data from a database. The input is the user's ID information, and the output is the corresponding user's healthcare data. Specific operations include retrieving data through a database query.

[1274] Step 6:

[1275] The server calculates nutritional balance based on the acquired nutritional component data and healthcare data. The input is nutritional component data and healthcare data, and the output is the result of the nutritional balance calculation. Specific operations include comparing the required amount of nutrients with the amount of intake and calculating deficiencies and excesses.

[1276] Step 7:

[1277] Voice input and facial expression data are sent from the user's device, and the server receives the data and analyzes it using an emotion engine. The input is voice data and facial expression data, and the output is the user's emotional state. Specifically, deep learning models for voice recognition and facial expression analysis are used.

[1278] Step 8:

[1279] The server generates dietary advice for the user based on the nutritional balance calculation results and the analyzed emotion data. The input is the nutritional balance calculation results and emotion data, and the output is customized dietary advice. Specific operations include an algorithm that takes both data into account and proposes an optimal meal plan.

[1280] Step 9:

[1281] The server notifies the user's device of the generated advice. The input is the generated dietary advice, and the output is a notification message to the user's device. Specific operations include sending push notifications and in-app messages.

[1282] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1283] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1284] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1285] [Fourth embodiment]

[1286] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1287] 7, a 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.

[1288] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1289] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1290] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1291] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1292] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1293] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1294] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1295] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1296] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1297] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1298] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1299] This invention is a system in which a user takes a photo of food or drink and sends the image to a server, which analyzes the image to identify the type and amount of food or drink, calculates the nutritional balance, and provides appropriate dietary advice.

[1300] Overall system configuration

[1301] The system mainly consists of three parts: the user's terminal, the server, and the database.

[1302] Users' devices, such as smartphones or tablets, have the ability to take photos of food and drinks and send the images to a server via a dedicated app.

[1303] The server analyzes the images, identifies the type and amount of food and drink, obtains nutritional data, compares it with the user's health care data to calculate nutritional balance, and generates dietary advice.

[1304] The database stores nutritional data for various foods and beverages, as well as healthcare data for each user.

[1305] Details of each function

[1306] 1. Take and send a photo

[1307] Users can take photos of food and drink using their smartphones or tablets, and then use a dedicated app to send the photos to the server.

[1308] For example, a user takes a photo of a vegetable salad and grilled chicken for lunch and uploads it to the server via the app.

[1309] 2. Image analysis and calculation of nutritional balance

[1310] The server analyzes the image sent and uses image analysis algorithms to identify the type and quantity of food and drink.

[1311] For example, deep learning techniques can be used to identify vegetable salad and grilled chicken from images.

[1312] Next, the system retrieves nutritional data for the identified food and drink from a food database, such as the vitamin C content of a vegetable salad or the protein content of grilled chicken.

[1313] 3. Matching with healthcare data

[1314] The server obtains the user's pre-registered healthcare data, including the user's age, gender, health status, and medical history.

[1315] The system calculates nutritional balance based on nutritional component data and health care data. For example, if a user has an iron deficiency, that deficiency will be reflected in the analysis results.

[1316] 4. Generating and notifying dietary advice

[1317] Based on the calculation results, the server generates dietary advice for the user, such as "We recommend adding spinach to supplement iron."

[1318] The advice is sent to the user's device, and the user can review the advice within the app and incorporate it into their next meal plan.

[1319] Specific examples

[1320] Example 1:

[1321] User A takes a photo of vegetable salad and grilled chicken for lunch. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies the vegetable salad and grilled chicken. It then retrieves the nutritional information for each item from a food database and compares it with User A's healthcare data. If it determines that User A is iron deficient, the server generates advice and notifies the user to add spinach and liver.

[1322] Example 2:

[1323] User B takes a photo of yogurt and fruit for breakfast. When the photo is sent to the server, the server analyzes the image and identifies the yogurt and fruit. The server then retrieves their nutritional information from a food database and compares it with User B's healthcare data to calculate nutritional balance. If the server determines that User B is calcium deficient, it generates and notifies User B with advice to add cheese or milk to supplement calcium.

[1324] In this way, the system is designed to allow users to easily check the nutritional balance of their meals and receive dietary advice tailored to their health condition, thereby improving the quality of their daily diet and preventing illness.

[1325] The processing flow will be explained below.

[1326] Program processing flow and specific operations

[1327] System-wide processing steps

[1328] Step 1:

[1329] Users use their devices to take photos of food and beverages, and it is important to use the camera app on their smartphone or tablet to capture clear images.

[1330] Step 2:

[1331] The user launches a dedicated app and takes a photo, sends it to the server, and the app packages the photo data as an HTTP request and uploads it to the server.

[1332] Step 3:

[1333] The server receives the uploaded photo data and temporarily stores it in a database, along with metadata (such as the time of submission, user ID, etc.).

[1334] Step 4:

[1335] The server runs an image analysis algorithm to analyze the stored photo data, which uses a deep learning model (e.g., CNN) to identify the type and quantity of food and drink.

[1336] Step 5:

[1337] The server retrieves nutritional information from the food database based on the identified food and drink data, for example, by querying the vitamin C content of a specified vegetable salad or the protein content of grilled chicken.

[1338] Step 6:

[1339] The server retrieves the user's healthcare data, including the user's age, gender, health status, and medical history, and uses a database query to retrieve pre-registered information.

[1340] Step 7:

[1341] The server calculates nutritional balance based on the acquired nutritional data and health care data. For example, if the user has an iron deficiency, the nutrition calculation will include that deficiency.

[1342] Step 8:

[1343] The server generates dietary advice for the user based on the calculation results, such as "We recommend adding spinach to supplement iron."

[1344] Step 9:

[1345] The server sends the generated advice to the user's device, where it is delivered as a push notification or in-app message.

[1346] Step 10:

[1347] Users can receive advice notifications on their device, open the app to view details, and take action to incorporate the advice into their next meal plan.

[1348] Through these steps, the system analyzes the user's diet and provides nutritional advice based on individual healthcare data to support a healthy diet.

[1349] Example 1

[1350] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1351] In today's busy lifestyles, many people face the challenge of properly managing their diet and nutritional balance. In particular, those who are deficient in certain nutrients or have a history of such deficiencies require appropriate nutritional management to improve their health. However, manually calculating nutritional information and generating dietary advice is cumbersome and requires specialized knowledge. Therefore, there is a need for a system that allows users to easily and efficiently manage their diet and receive appropriate dietary advice.

[1352] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1353] In this invention, the server includes an image acquisition means, a data transmission means, an image analysis means, an information acquisition means, a data acquisition means, a nutritional balance calculation means, a guidance generation means, and a data notification means. Based on image data of food and drink photographed by a user, the server automatically identifies the type and quantity of food and drink, acquires nutritional information, calculates nutritional balance based on the user's health management data, and generates and notifies the user of appropriate dietary guidance. This allows users to efficiently manage their eating habits and receive dietary guidance tailored to their health condition.

[1354] 1. "User" means an individual who uses the system to take photos of their own food and drink and receive dietary advice.

[1355] 2. "Terminal" means a smartphone, tablet, or other device with an image capture function that transmits image data captured by the user to the server.

[1356] 3. "Image acquisition means" refers to the functions and applications that allow users to take images of food and beverages using a terminal.

[1357] 4. "Data transmission means" refers to the communication protocol and functions for transmitting image data captured by the terminal to the server.

[1358] 5. "Server" means a computer system that analyzes received image data, identifies the type and quantity of food and beverages, calculates nutritional balance, and generates dietary advice.

[1359] 6. "Image analysis means" means software and algorithms that analyze image data within the server and identify the type and quantity of food and beverages.

[1360] 7. "Food Information Database" refers to a database that records nutritional information for various foods and beverages.

[1361] 8. "Information acquisition means" refers to the function or process of acquiring nutritional information of the food or beverage identified by the image analysis means from the food information database.

[1362] 9. "Health Management Data" means information about a user's health, such as the user's age, gender, health status, and medical history.

[1363] 10. "Data Capture Method" means a function or process for capturing pre-recorded user health management data.

[1364] 11. "Nutritional balance calculation means" refers to software or algorithms for calculating a user's nutritional balance based on acquired nutritional information and health management data.

[1365] 12. "Guidance generation means" refers to the functions and processes for generating specific dietary advice for the user based on the calculation results of the nutritional balance calculation means.

[1366] 13. "Data notification means" refers to the communication protocols and functions for sending the generated dietary advice to the user's terminal and notifying the user.

[1367] This invention is a system in which a user takes an image of food and drink and sends the image to a server, which analyzes the image to identify the type and quantity of food and drink, calculates the nutritional balance, and provides appropriate dietary advice.

[1368] The entire system is mainly composed of a user terminal, a server, a food information database, and a health management database. Specifically, the roles and processes of each component are as follows:

[1369] User's device

[1370] The user's device is a device such as a smartphone or tablet. A dedicated app is installed on this device and has the following functions:

[1371] 1. Image acquisition method: Users use this dedicated app to take images of food and drink.

[1372] 2. Data transmission means: The captured image data is transmitted to the server using the HTTPS protocol.

[1373] server

[1374] The server analyzes the received image data using the following various means and provides dietary advice to the user.

[1375] 1. Image analysis means: The server analyzes the received image data using a deep learning algorithm (e.g., TensorFlow or PyTorch) to identify the type and quantity of food and beverages.

[1376] 2. Information acquisition means: The server refers to the food information database and acquires nutritional information of the identified food or drink.

[1377] 3. Data acquisition means: The server acquires the user's health management data, including age, gender, health status, and medical history, from the database.

[1378] 4. Nutritional balance calculation means: The server calculates nutritional balance based on the acquired nutritional component information and health management data.

[1379] 5. Guidance generation means: Based on the calculation results, the server generates specific dietary advice for the user.

[1380] 6. Data notification method: The generated dietary advice is sent to the user's device via a dedicated app.

[1381] Database

[1382] 1. Food information database: A database that records nutritional information for various foods and beverages.

[1383] 2. Health management database: A database that records information about the user's health.

[1384] Specific examples

[1385] 1. Example 1:

[1386] User A takes image data of vegetable salad and grilled chicken for lunch.

[1387] Image data is sent to the server using a dedicated app.

[1388] The server analyzes the image and identifies the vegetable salad and grilled chicken. It then retrieves the nutritional information for each from a food information database and compares it with User A's health management data.

[1389] The server confirms that User A is iron deficient, generates dietary advice such as "We recommend adding spinach or liver to supplement your iron intake," and notifies the user's device.

[1390] 2. Example 2:

[1391] User B takes an image of yogurt and fruit for breakfast.

[1392] Image data is sent to the server using a dedicated app.

[1393] The server analyzes the image, identifies the yogurt and fruit, retrieves their nutritional information from a food information database, and compares it with User B's health management data to calculate the nutritional balance.

[1394] The server confirms that User B is calcium deficient, generates dietary advice such as "We recommend adding cheese or milk to supplement calcium," and notifies the user's device.

[1395] Example prompts (for generative AI models)

[1396] "I took a photo of my lunch and uploaded it. The analysis showed that it was a vegetable salad and grilled chicken. Please tell me the detailed nutritional balance."

[1397] "I sent a photo of my breakfast: yogurt and fruit. Please provide dietary advice that takes my health condition into consideration."

[1398] This system allows users to easily manage their eating habits and receive dietary advice based on their health condition, thereby improving the quality of their daily eating habits and achieving proper nutritional management.

[1399] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1400] Step 1:

[1401] Users take images of food and drink using a dedicated app on their smartphone or tablet. The input is image data of the food and drink, and the output is the image data. Specifically, the user takes a photo of their lunch and saves the image data in JPEG format.

[1402] Step 2:

[1403] The device sends the captured image data to the server using the HTTPS protocol. The input is the captured image data, and the output is the transmission of the image data to the server. For example, the device uploads the captured image to the server via a dedicated app.

[1404] Step 3:

[1405] The server analyzes the received image data. Using a deep learning model (e.g., TensorFlow or PyTorch), it identifies the type and quantity of food and drink from the image. The input is the sent image data, and the output is the type and quantity of identified food and drink. Specifically, it recognizes vegetable salad and grilled chicken through image analysis.

[1406] Step 4:

[1407] The server references the food information database to obtain nutritional information for the identified food or drink. The input is the type of food or drink identified, and the output is the obtained nutritional information. For example, the vitamin C content of a vegetable salad or the protein content of grilled chicken is obtained.

[1408] Step 5:

[1409] The server refers to the health management database to obtain the user's health management data. The input is the user's identification information, and the output is the user's health management data. For example, the server obtains information about User A's age, gender, and iron deficiency.

[1410] Step 6:

[1411] The server calculates nutritional balance based on the acquired nutritional information and health management data. The input is nutritional information and health management data, and the output is the calculated nutritional balance. For example, taking into account User A's iron deficiency, the server comprehensively evaluates the nutritional data of the vegetable salad and grilled chicken they ate.

[1412] Step 7:

[1413] The server generates specific dietary advice for the user based on the calculation results. The input is the calculation result of nutritional balance, and the output is the generated dietary advice. Specifically, it generates advice such as "We recommend adding spinach or liver to supplement iron."

[1414] Step 8:

[1415] The device receives the dietary advice sent from the server and displays it in the dedicated app. The input is the dietary advice from the server, and the output is what is displayed to the user. The user receives a push notification and can check the specific dietary advice in the dedicated app.

[1416] (Application example 1)

[1417] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1418] Many people today suffer from poor eating habits and a lack of nutritional balance, making nutritional management difficult, especially when eating out frequently at cafes and restaurants. Furthermore, there is a lack of systems that allow users to receive accurate dietary advice tailored to their individual health conditions. It is necessary to provide a support system that solves this problem and enables users to choose healthy meals even when eating out.

[1419] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1420] In this invention, the server includes a terminal means for a user to take photos of food and beverages, a transmission means for transmitting the photographed photos to the server, an image analysis means in the server for analyzing the transmitted photos and identifying the type and amount of food and beverages, a nutritional data acquisition means for acquiring nutritional data of the identified food and beverages by referring to a food database, a healthcare data acquisition means for acquiring health care data of the user registered in advance, a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional data and the healthcare data, an advice generation means for generating dietary advice for the user based on the calculation results, a notification means for transmitting the generated advice to the user's terminal, and a supplemental food suggestion means for analyzing photos of food and beverages taken by the user, retrieving related supplemental nutritional information from an external database based on the acquired nutritional data, and suggesting foods that optimize nutritional balance. This allows users to receive dietary advice suited to their own health condition even when eating out, helping them to achieve a nutritionally balanced diet.

[1421] "Terminal means" refers to a device that a user uses to take a photo of food or drink and send the photo to the server. Specifically, it refers to a mobile information terminal such as a smartphone or tablet.

[1422] "Transmission means" refers to the function or process for sending photos taken by the user to the server, including internet communication functions and send buttons within the application.

[1423] "Image analysis means" refers to the technology or algorithms used to analyze the submitted photograph and identify the type and quantity of food or drink, including image recognition technology using deep learning.

[1424] "Nutrition data acquisition means" refers to a function that acquires nutritional component data of the identified food or drink by referring to a food database.

[1425] "Means for obtaining health care data" refers to the function of obtaining the user's pre-registered health care data (age, gender, health condition, medical history, etc.).

[1426] "Nutritional balance calculation means" refers to the technology or algorithm for calculating nutritional balance based on acquired nutritional component data and healthcare data.

[1427] "Advice generation means" refers to a function that generates dietary advice for the user based on the results of nutritional balance calculations.

[1428] "Notification means" refers to a function for sending the generated advice to the user's terminal and notifying the user.

[1429] "Supplementary food suggestion means" refers to a function that analyzes photos of food and beverages taken by the user, obtains related supplementary nutritional information from an external database based on the acquired nutritional data, and suggests foods that will optimize nutritional balance.

[1430] The system that realizes this application example allows the user to take a photo of food or drink and send the image to a server, which then analyzes the image to identify the type and amount of food or drink, calculates the nutritional balance, and provides appropriate dietary advice.

[1431] System configuration

[1432] The system consists of three main parts: the user's terminal, the server, and the database.

[1433] 1. User's Device

[1434] Users take photos of food and drink using a mobile information terminal such as a smartphone and send the photos to the server via a dedicated application, which then uses its internet communication function to send the photo data to the server.

[1435] 2. Server

[1436] Image analysis methods

[1437] The server receives the image and analyzes it using deep learning techniques, including libraries such as TensorFlow and Keras, to identify the type and quantity of food and drink.

[1438] Nutritional data acquisition method

[1439] The server references a food database based on the identified food and drink to obtain nutritional information, which may also be obtained from an external nutrition information API (e.g., "nutrition-api.com").

[1440] Healthcare data acquisition method

[1441] The server acquires the user's pre-registered health care data (age, gender, health condition, medical history, etc.), which is stored in a database.

[1442] Nutritional Balance Calculator

[1443] The server calculates nutritional balance based on the acquired nutritional data and healthcare data, using an algorithm created in Python or other programs.

[1444] Advice Generation Method

[1445] The server generates dietary advice based on the calculation results. The advice generated suggests nutritional supplements tailored to the user's health condition. For example, if the user is iron deficient, it will recommend adding spinach and liver.

[1446] Notification means

[1447] The server then sends the generated advice to the user's device via a dedicated application.

[1448] Supplementary food suggestion means

[1449] The server analyzes photos of food and drink taken by the user, retrieves relevant supplemental nutritional information from an external database based on the acquired nutritional data, and suggests foods to optimize nutritional balance, taking into account the user's health care data.

[1450] Specific examples

[1451] Specific examples are shown below.

[1452] 1. Example 1: A user takes a photo of "chicken salad" at a cafe. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies it as "chicken salad." The server then retrieves the nutritional data for "chicken salad" from the nutrition information API and identifies the user's nutritional deficiency (e.g., iron deficiency). The server generates advice recommending "add spinach" to supplement iron and notifies the user's device.

[1453] Prompt Sentence Examples

[1454] "Please analyze photos of food and drink provided by the user and identify the type of food and drink. Retrieve nutritional information for the identified food and drink from an external API, and match it with the user's health data to suggest specific foods to supplement any nutrient deficiencies."

[1455] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1456] Step 1:

[1457] The user takes a photo of the food or drink.

[1458] Input: Image data captured by a user using a mobile information device such as a smartphone.

[1459] Output: Captured image data.

[1460] Specific actions: A user takes a photo of a "chicken salad" at a cafe. Using the camera application on their smartphone, they take a photo that captures the entire food item.

[1461] Step 2:

[1462] The captured photo is sent to the server.

[1463] Input: Captured image data.

[1464] Output: Image data sent to the server.

[1465] Specific operation: The user opens the dedicated application and presses the "send button" to send the photo they have taken to the server. The application then uploads the photo to the server via the Internet.

[1466] Step 3:

[1467] The server analyzes the photos sent and identifies the type and quantity of food and drink.

[1468] Input: Image data sent to the server.

[1469] Output: Data on the type and quantity of food and drink consumed.

[1470] How it works: The server analyzes the received image data using a deep learning model (e.g., using TensorFlow or Keras). The model identifies the type of food in the image (e.g., "chicken salad") and estimates its quantity.

[1471] Step 4:

[1472] The server refers to the food database to obtain nutritional component data of the identified food or drink.

[1473] Input: Data about the type of food or drink.

[1474] Output: Nutritional information for food and drink.

[1475] Specific behavior: The server requests and obtains the nutritional information for the specified food or drink from a food database or an external nutrition information API (e.g., "nutrition-api.com"). For example, it obtains the calorie and macronutrient data for "chicken salad."

[1476] Step 5:

[1477] The server acquires healthcare data of pre-registered users.

[1478] Input: User's identity information.

[1479] Output: User's healthcare data.

[1480] Specific operation: The server accesses the database and retrieves healthcare data (age, gender, health status, medical history, etc.) based on the user's ID. For example, the healthcare data may confirm that the user is iron deficient.

[1481] Step 6:

[1482] The server calculates nutritional balance based on the acquired nutritional component data and health care data.

[1483] Input: Nutritional information of food and drink and user's healthcare data.

[1484] Output: Nutritional balance data.

[1485] How it works: The server uses programs such as Python to compare the nutritional information of food and drink with the user's health data and calculate nutrient deficiencies or excesses. For example, it calculates the iron deficiency that occurs when eating chicken salad alone.

[1486] Step 7:

[1487] The server generates dietary advice for the user based on the calculation results.

[1488] Input: Nutritional balance data.

[1489] Output: Dietary advice.

[1490] Specific action: The server generates advice suggesting specific foods to supplement the missing nutrients, for example, recommending "add spinach to supplement iron."

[1491] Step 8:

[1492] The server sends the generated advice to the user's terminal for notification.

[1493] Enter: dietary advice.

[1494] Output: An advisory notification that is displayed on the user's device.

[1495] How it works: The server sends advice to the user's smartphone via a dedicated application, and the user can then check the advice within the app.

[1496] Step 9:

[1497] The server analyzes photos of food and drink taken by the user, retrieves relevant supplemental nutritional information from an external database based on the acquired nutritional data, and suggests foods that will optimize nutritional balance.

[1498] Input: Food and beverage type data and nutritional information.

[1499] Output: Food suggestion data to optimize nutritional balance.

[1500] Specific operation: Based on the analysis results, the server retrieves food information corresponding to the missing nutrients from an external database (e.g., "nutrition-api.com") and suggests supplementary foods (e.g., "spinach" to supplement iron).

[1501] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1502] This invention combines a system that takes photos of food and drink, analyzes the transmitted images to calculate nutritional balance, and provides personalized dietary advice with an emotion engine that recognizes the user's emotions and customizes dietary advice based on their emotional state.

[1503] Overall system configuration

[1504] The system mainly consists of four parts: the user's terminal, the server, the database, and the emotion engine.

[1505] Users' devices, such as smartphones or tablets, have the ability to take photos of food and drinks and send the images to a server via a dedicated app.

[1506] The server analyzes the images, identifies the type and amount of food and drink, obtains nutritional data, compares it with the user's health care data and emotional state to calculate nutritional balance and generate dietary advice.

[1507] The database stores nutritional data for various foods and beverages, as well as healthcare data for each user.

[1508] The emotion engine has the function of analyzing the user's emotional state and providing that information to the server.

[1509] Details of each function

[1510] 1. Take and send a photo

[1511] Users can take photos of food and drink using their smartphones or tablets, and then use a dedicated app to send the photos to the server.

[1512] For example, a user takes a photo of a vegetable salad and grilled chicken for lunch and uploads it to the server via the app.

[1513] 2. Image analysis and calculation of nutritional balance

[1514] The server analyzes the image sent and uses image analysis algorithms to identify the type and quantity of food and drink.

[1515] For example, deep learning techniques can be used to identify vegetable salad and grilled chicken from images.

[1516] 3. Obtaining nutritional data

[1517] The server retrieves nutritional data for the identified food or drink from a food database, such as the vitamin C content of a vegetable salad or the protein content of grilled chicken.

[1518] 4. Healthcare data acquisition and collation

[1519] The server obtains the user's pre-registered healthcare data, including the user's age, gender, health status, and medical history.

[1520] Nutritional balance is calculated based on nutritional component data and health care data. For example, if a user has an iron deficiency, the nutritional calculation will include that deficiency.

[1521] 5. Acquiring and matching emotion data

[1522] The emotion engine recognizes the user's emotional state and sends that information to the server using the user's voice input, facial expressions, or other biometric data.

[1523] For example, voice recognition technology can be used to analyze the tone and pace of a user's voice to identify their emotional state.

[1524] 6. Generating and customizing dietary advice

[1525] The server generates optimal dietary advice for the user based on the nutritional balance calculation results and emotional state.

[1526] For example, if a user is feeling stressed, the system generates advice recommending foods that have a relaxing effect.

[1527] 7. Notices and Displays

[1528] The server sends the generated advice to the user's device, where it is delivered as a push notification or in-app message.

[1529] Users can receive advice notified on their device, open the app to check the details, and incorporate them into their next meal plan.

[1530] Specific examples

[1531] Example 1:

[1532] User A takes a photo of vegetable salad and grilled chicken for lunch. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies the vegetable salad and grilled chicken. It then retrieves the nutritional information for each item from a food database and compares it with User A's healthcare data. The emotion engine then recognizes User A's emotional state and sends it to the server. The server, having determined that User A is feeling stressed, generates and notifies User A of advice recommending foods that have a relaxing effect.

[1533] Example 2:

[1534] User B takes a photo of yogurt and fruit for breakfast. When the photo is sent to the server, the server analyzes the image and identifies the yogurt and fruit. It then retrieves their nutritional information from a food database and compares it with User B's healthcare data to calculate nutritional balance. The emotion engine then recognizes User B's emotional state and sends it to the server. The server, having confirmed that User B is feeling happy, generates and notifies User B of advice recommending foods to help maintain that mood.

[1535] The system is designed to allow users to easily check the nutritional balance of their meals and receive dietary advice tailored to their health and emotional state, helping to improve the quality of their daily diet and prevent illness.

[1536] The processing flow will be explained below.

[1537] Program processing flow and specific operations

[1538] System-wide processing steps

[1539] Step 1:

[1540] The user uses the device to take a photo of the food or drink. The user launches the camera app on their smartphone or tablet and takes a photo of the food or drink.

[1541] Step 2:

[1542] The user launches the dedicated app and sends the photos they have taken to the server, which then uploads the photo data to the server in the form of an HTTP request.

[1543] Step 3:

[1544] The server receives the uploaded photo data and temporarily stores it in a database, while also recording metadata such as the time of submission and user ID.

[1545] Step 4:

[1546] The server runs an image analysis algorithm, using a deep learning model (e.g., CNN) to identify the type and quantity of food and drink. The identified type and quantity are then stored in a database.

[1547] Step 5:

[1548] The server queries a food database to obtain nutritional information for the identified food or drink, such as the vitamin C content of a vegetable salad or the protein content of grilled chicken.

[1549] Step 6:

[1550] The server retrieves the healthcare data of pre-registered users, using a database query to obtain a dataset containing the user's age, gender, health status, and medical history.

[1551] Step 7:

[1552] The server calculates nutritional balance based on the acquired nutritional data and health care data. For example, if the user has an iron deficiency, the nutrition calculation will take that deficiency into account.

[1553] Step 8:

[1554] The emotion engine recognizes the user's emotional state. Emotion recognition algorithms are run to analyze the user's voice input, facial expressions, or other biometric data.

[1555] Step 9:

[1556] The emotion engine sends the user's recognized emotion data to the server, along with metadata about the user's emotional state.

[1557] Step 10:

[1558] The server combines the nutritional balance calculation results with data from the emotion engine. Taking into account the user's emotional state, it generates appropriate dietary advice. For example, it recommends foods with a relaxing effect to a user who is feeling stressed.

[1559] Step 11:

[1560] The server then sends the generated dietary advice to the user's device in the form of a push notification or an in-app message.

[1561] Step 12:

[1562] Users can receive advice notifications on their device, open the app to view details, and use the advice to plan their next meal.

[1563] Through these steps, the system analyzes the user's diet and provides customized nutritional advice based on individual healthcare and emotional data, supporting a healthy diet.

[1564] Example 2

[1565] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1566] Conventional dietary advice systems provide advice based only on the nutritional balance of foods and beverages, making it difficult to provide detailed dietary advice that reflects the user's emotional state. Therefore, there is a need for a method that generates optimal dietary advice that reflects the user's emotional state and stress level, thereby improving the user's overall health.

[1567] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1568] In this invention, the server includes emotion analysis means for analyzing the emotional state of the user, nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional component data and biological information, and advice generation means for generating dietary advice for the user based on the nutritional balance calculation result and the emotional state, thereby making it possible to provide more precise and personalized dietary advice that also takes the emotional state of the user into consideration.

[1569] "User" refers to a person using this system.

[1570] "Terminal means" refers to an electronic device that allows a user to take pictures of food and drink and send them to the server.

[1571] "Photographing means" refers to a function that allows a user to photograph an image using a terminal means.

[1572] "Transmission means" refers to a function for transmitting captured images to a server.

[1573] "Server" refers to the computer system that analyzes images sent by users, acquires and processes various data, and generates dietary advice.

[1574] "Image analysis means" refers to a function that analyzes the transmitted image and identifies the type and amount of food and drink.

[1575] "Food database" refers to a database that stores nutritional information for various foods and beverages.

[1576] "Nutrition data acquisition means" refers to a function for acquiring nutritional component data of a specified food or drink from a food database.

[1577] "Biometric data acquisition means" refers to a function that acquires biometric information such as the age, gender, health condition, and medical history of a pre-registered user.

[1578] "Nutritional balance calculation means" refers to a function that calculates nutritional balance based on the acquired nutritional component data and the user's biological information.

[1579] "Emotion analysis means" refers to a function that recognizes the user's emotional state and provides that data to the server.

[1580] "Advice generation means" refers to a function that generates optimal dietary advice for a user based on the nutritional balance calculation results and emotional state.

[1581] "Notification means" refers to a function that sends the generated advice to the user's terminal.

[1582] MODE FOR CARRYING OUT THE INVENTION

[1583] This invention is a system that allows users to take photos of food and drink and send them to a server, which uses image analysis algorithms to calculate nutritional balance and provide personalized dietary advice. Furthermore, it can recognize the user's emotional state and use that information to better customize dietary advice.

[1584] Overall system configuration

[1585] The system mainly consists of four parts: the user's terminal, the server, the database, and the sentiment analysis engine.

[1586] 1. User's Device

[1587] The user's device is a smartphone or tablet, which has the ability to take pictures of food and drink and send them to the server via a dedicated app that is equipped with a camera function and a send button.

[1588] 2. Server

[1589] The server is responsible for image analysis, data acquisition, advice generation and notification functions.

[1590] Deep learning technologies such as TensorFlow are used for image analysis.

[1591] The Python NumPy library is used to match biological data with nutritional data and calculate nutritional balance.

[1592] For emotion recognition, we use the Google Speech-to-Text API and other tools to extract emotions from voice.

[1593] 3. Database

[1594] The database stores nutritional data for various foods and beverages, as well as biometric information for each user (age, gender, health status, medical history).

[1595] For example, obtain nutritional data such as protein and vitamin C for grilled chicken from a food database.

[1596] 4. Sentiment Analysis Engine

[1597] The emotion analysis engine has the function of recognizing the user's emotional state from their voice input and facial expression recognition, and providing that information to the server.

[1598] This allows the system to provide dietary advice based on the user's emotional state, such as stress or happiness.

[1599] Specific examples

[1600] Example 1: User A takes a photo of vegetable salad and grilled chicken for lunch. When the photo is sent to the server using a dedicated app, the server analyzes the image and identifies the vegetable salad and grilled chicken. The server then retrieves the nutritional information for each item from the food database and compares it with User A's biometric information to calculate the nutritional balance. Furthermore, the emotion analysis engine recognizes User A's emotional state and confirms that he or she is feeling stressed. The server generates advice recommending foods with a relaxing effect and sends it to the user via push notification.

[1601] Example 2: User B takes a photo of yogurt and fruit for breakfast. The photo sent to the server is analyzed and identified as yogurt and fruit. Nutritional information is then retrieved from the food database and matched with User B's biometric information. The sentiment analysis engine recognizes User B's happy emotional state and generates advice recommending foods to maintain that state, notifying the user via an in-app message.

[1602] This allows users to easily check the nutritional balance of their meals and receive dietary advice appropriate to their health and emotional state.

[1603] Prompt Sentence Examples

[1604] "You take a photo of your food or drink and send the image to our server. The system then analyzes the image and provides dietary advice based on your nutritional balance and emotional state."

[1605] The system allows users to better manage their health and make dietary choices that suit their emotional state.

[1606] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1607] Program processing flow

[1608] Step 1: Take and send a photo

[1609] Users take photos of food and drinks using their smartphones or tablets.

[1610] Specific operation: The user taps the "Take a photo" button in the dedicated app to launch the camera, takes a photo of the food or drink, and presses the "Send" button.

[1611] Input: A photo of food or drink taken by the user

[1612] Output: Food and drink images sent to the server

[1613] Step 2: Receiving and analyzing images

[1614] The server receives the image sent by the user and analyzes it using image analysis algorithms.

[1615] What it does: The server uses an image analysis library (e.g., TensorFlow) to identify food and drink objects in the image and determine their type and quantity.

[1616] Input: Food and drink images sent from the user's device

[1617] Output: Analyzed food and drink types and quantities

[1618] Step 3: Obtaining nutritional data

[1619] Based on the results of the image analysis, the server retrieves nutritional data for the identified food and beverage from a food database.

[1620] Specific operation: The server queries the food database to obtain nutritional information for food and beverage items (e.g., grilled chicken, vegetable salad, etc.).

[1621] Input: Type and quantity of food and drink analyzed

[1622] Output: Nutritional information

[1623] Step 4: Acquire biometric data

[1624] The server obtains the biometric data of pre-registered users, including age, gender, health status, and medical history.

[1625] Specific operation: The server retrieves the user's biometric information from the user database using an SQL query.

[1626] Input: User's ID

[1627] Output: User biometric data

[1628] Step 5: Calculate your nutritional balance

[1629] The server calculates the nutritional balance based on the acquired nutritional component data and biological information.

[1630] Specific operation: The server combines nutritional data and biological data and calculates nutritional balance using Python's NumPy library.

[1631] Input: Nutritional information and user biometric data

[1632] Output: Nutritional balance calculation results

[1633] Step 6: Obtaining emotion data

[1634] The emotion analysis engine recognizes the user's emotional state and sends the data to the server, using voice input, facial expressions, and biometric data.

[1635] How it works: Users record voice messages within the app, and the server passes the voice data to an emotion analysis engine to extract emotional states.

[1636] Input: User's voice or facial expression data

[1637] Output: Perceived emotional state of the user

[1638] Step 7: Generate dietary advice

[1639] The server generates optimal dietary advice for the user based on the nutritional balance calculation results and emotional state.

[1640] Specific operation: The server uses an advice generation algorithm to integrate nutritional balance and emotional state to generate customized advice.

[1641] Input: Nutritional balance calculation results and emotional state

[1642] Output: Optimal dietary advice

[1643] Step 8: Notifications and Display

[1644] The server sends the generated advice to the user's device, where it is delivered as a push notification or in-app message.

[1645] How it works: The server sends advice in JSON format to the user's device, which is then received by a dedicated app. The app then uses the notification API to generate a push notification, which the user can tap to display details.

[1646] Enter: Best Dietary Advice

[1647] Output: Advice displayed on the user's terminal

[1648] (Application example 2)

[1649] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1650] Managing employee health is a very important issue in manufacturing sites. Employees' emotional state is also an important factor, as it directly impacts work efficiency and safety. However, there is currently no way for employees to easily check the nutritional balance of their daily meals and receive appropriate dietary advice tailored to their emotional state. A system to improve this situation is needed.

[1651] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a terminal means for a user to take photos of food and beverages, a transmission means for sending the taken photos to the server, an image analysis means for analyzing the sent photos and identifying the type and amount of food and beverages, a nutritional data acquisition means for acquiring nutritional data of the identified food and beverages by referring to a food database, a healthcare data acquisition means for acquiring health care data of a pre-registered user, a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional data and health care data, an emotion analysis means for customizing dietary advice based on the acquired emotion data, an advice generation means for generating dietary advice for the user based on the calculation results and the emotion data, and a notification means for sending the generated advice to the user's terminal. This makes it possible to provide dietary advice optimized for the health and emotional state of employees, thereby improving work efficiency and safety in the factory.

[1652] A "user terminal" is a device that has the function of taking photos of food and drink and sending the images to a server.

[1653] "Transmission means" refers to a method or device that has the function of transmitting a photograph taken from a user's terminal to a server.

[1654] "Image analysis means" refers to an algorithm or device that has the function of analyzing the transmitted photograph and identifying the type and quantity of food and drink.

[1655] The "nutrition data acquisition means" refers to a method or device that acquires nutritional component data of a specified food or drink by referring to a food database.

[1656] The "healthcare data acquisition means" refers to a method or device for acquiring healthcare data of a user who has been registered in advance.

[1657] The "nutritional balance calculation means" refers to an algorithm or device that has the function of calculating nutritional balance based on the acquired nutritional component data and healthcare data.

[1658] The "emotion analysis means" is an algorithm or device that has the function of customizing dietary advice based on acquired emotion data.

[1659] The "advice generation means" refers to a method or device that generates dietary advice for a user based on the calculation results and emotion data.

[1660] The "notification means" is a method or device that has the function of transmitting the generated advice to the user's terminal.

[1661] This invention is implemented as a system for providing dietary advice based on the health management and emotional state of employees in a factory. The system mainly consists of a user terminal, a server, a database, and an emotion engine.

[1662] User's device

[1663] The user's device is a mobile device such as a smartphone that has the function of taking photos of the food and beverages served in the factory cafeteria. A dedicated application is also installed on this device, allowing the user to send the photos to the server.

[1664] server

[1665] The server has the following functions:

[1666] Image analysis means: Deep learning technology is used to analyze photos of food and drink sent from the user's device to identify the type and quantity of food and drink, and to obtain nutritional data for the identified food and drink from a food database.

[1667] Healthcare data acquisition means: Acquires healthcare data of pre-registered users from a database.

[1668] Emotion analysis means: The emotional state of the user is analyzed using an emotion engine based on data such as voice input and facial expressions obtained from the user's device.

[1669] Nutritional balance calculation means: Calculates the user's nutritional balance based on the acquired nutritional data and health care data.

[1670] Advice generation means: Based on the calculation results and the analyzed emotion data, dietary advice is generated for the user.

[1671] Notification method: The generated advice is sent to the user's device in the form of a push notification or similar.

[1672] Database

[1673] The database stores nutritional data for various foods and beverages, as well as healthcare data for each user. The food database consists of multiple lists, each containing detailed information such as the nutritional content and calories of each food.

[1674] Emotion Engine

[1675] The emotion engine uses a deep learning model to recognize the user's emotional state. The engine analyzes the user's emotional state based on their voice tone and facial expression data, and provides this information to the server.

[1676] Specific examples

[1677] For example, an employee takes a photo of the food served in the cafeteria at lunchtime with their smartphone and sends it to a server via a dedicated app. At this time, the user uses voice input to record their emotional state. The server analyzes the sent photo and voice data to identify the type and amount of food and drink and the user's emotional state. It then retrieves the necessary data from a food database and a healthcare database and calculates nutritional balance. It uses an emotion engine to analyze the user's emotional state and generates optimal dietary advice based on that. Finally, the generated advice is pushed to the user's device.

[1678] Prompt Sentence Examples

[1679] Image path: . / lunch.jpg

[1680] Path to the audio data: . / user_voice.wav

[1681] User ID: employee_12345

[1682] In this way, a system can be designed that allows employees to easily check the nutritional balance of their meals and receive dietary advice tailored to their health and emotional state, thereby improving the quality of their daily diet and increasing work efficiency and safety in the factory.

[1683] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1684] Step 1:

[1685] A user takes a photo of food or drink using a mobile device such as a smartphone. At this time, a dedicated application is launched and image data of the food or drink is acquired using the built-in camera. The input is the photo of the food or drink (image data), and the output is the image taken by the user.

[1686] Step 2:

[1687] The user sends the photographic data of the food or drink they have taken to the server via a dedicated application. The input is the image data they have just taken, and the output is the image data sent to the server.

[1688] Step 3:

[1689] The server receives the transmitted photo data and uses an image analysis algorithm to identify the types and quantities of food and drink contained in the photo. The input is the received image data, and the output is a list of identified food and drink items and their quantities. Specific operations include image recognition processing using a deep learning model.

[1690] Step 4:

[1691] The server references a food database to retrieve nutritional data related to the identified foods and beverages. The input is a list of the identified foods and beverages, and the output is the nutritional data corresponding to each food and beverage. Specific operations include retrieving data through a database query.

[1692] Step 5:

[1693] The server retrieves pre-registered user healthcare data from a database. The input is the user's ID information, and the output is the corresponding user's healthcare data. Specific operations include retrieving data through a database query.

[1694] Step 6:

[1695] The server calculates nutritional balance based on the acquired nutritional component data and healthcare data. The input is nutritional component data and healthcare data, and the output is the result of the nutritional balance calculation. Specific operations include comparing the required amount of nutrients with the amount of intake and calculating deficiencies and excesses.

[1696] Step 7:

[1697] Voice input and facial expression data are sent from the user's device, and the server receives the data and analyzes it using an emotion engine. The input is voice data and facial expression data, and the output is the user's emotional state. Specifically, deep learning models for voice recognition and facial expression analysis are used.

[1698] Step 8:

[1699] The server generates dietary advice for the user based on the nutritional balance calculation results and the analyzed emotion data. The input is the nutritional balance calculation results and emotion data, and the output is customized dietary advice. Specific operations include an algorithm that takes both data into account and proposes an optimal meal plan.

[1700] Step 9:

[1701] The server notifies the user's device of the generated advice. The input is the generated dietary advice, and the output is a notification message to the user's device. Specific operations include sending push notifications and in-app messages.

[1702] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1703] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1704] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1705] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1706] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1707] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1708] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1709] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1710] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1711] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1712] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1713] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1714] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1715] 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.

[1716] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1717] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1718] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1719] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1720] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1721] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1722] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1723] The following is further disclosed regarding the above embodiment.

[1724] Claims

[1725] (Claim 1)

[1726] a terminal means for a user to take a photo of food and drink;

[1727] a transmission means for transmitting the photographed photo to a server;

[1728] an image analysis means in the server for analyzing the transmitted photograph and identifying the type and amount of food and drink;

[1729] a nutritional data acquisition means for acquiring nutritional component data of the identified food and drink by referring to a food database;

[1730] A healthcare data acquisition means for acquiring healthcare data of a pre-registered user;

[1731] a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional component data and health care data;

[1732] an advice generating means for generating dietary advice for the user based on the calculation results;

[1733] a notification means for transmitting the generated advice to a user's terminal;

[1734] A system including:

[1735] (Claim 2)

[1736] 10. The system of claim 1, further comprising a healthcare data acquisition means, wherein the user's healthcare data includes the user's age, gender, health condition, and medical history.

[1737] (Claim 3)

[1738] 2. The system according to claim 1, wherein the advice generating means generates an indication for the user such as "recommended to take a small amount" or "recommended to take a large amount" based on nutritional balance.

[1739] "Example 1"

[1740] (Claim 1)

[1741] Image acquisition means for a user to capture image data of food and drink;

[1742] a data transmission means for transmitting the captured image data to a server;

[1743] an image analysis means for analyzing the image data received by the server and identifying the type and quantity of food and drink;

[1744] an information acquisition means for acquiring nutritional component information of the identified food and drink by referring to a food information database;

[1745] a data acquisition means for acquiring pre-recorded health management data of the user;

[1746] a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional component information and health management data;

[1747] an advice generating means for generating dietary advice for the user based on the calculation results;

[1748] a data notification means for transmitting the generated instruction to a user's terminal;

[1749] A system including:

[1750] (Claim 2)

[1751] 2. The system of claim 1, further comprising a data acquisition means, wherein the user's health management data includes the user's age, gender, health status, and medical history.

[1752] (Claim 3)

[1753] 2. The system according to claim 1, wherein the guidance generating means generates an index that recommends to the user to increase or decrease food intake based on nutritional balance.

[1754] "Application Example 1"

[1755] (Claim 1)

[1756] a terminal means for a user to take a photo of food and drink;

[1757] a transmission means for transmitting the photographed photo to a server;

[1758] an image analysis means in the server for analyzing the transmitted photograph and identifying the type and amount of food and drink;

[1759] a nutritional data acquisition means for acquiring nutritional component data of the identified food and drink by referring to a food database;

[1760] A healthcare data acquisition means for acquiring healthcare data of a pre-registered user;

[1761] a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional component data and health care data;

[1762] an advice generating means for generating dietary advice for the user based on the calculation results;

[1763] a notification means for transmitting the generated advice to a user's terminal;

[1764] a supplemental food suggestion means for analyzing a photograph of food or drink taken by a user, retrieving related supplemental nutritional information from an external database based on the acquired nutritional component data, and suggesting foods that optimize nutritional balance;

[1765] A system including:

[1766] (Claim 2)

[1767] 10. The system of claim 1, further comprising a healthcare data acquisition means, wherein the user's healthcare data includes the user's age, gender, health condition, and medical history.

[1768] (Claim 3)

[1769] 2. The system according to claim 1, wherein the advice generating means generates an indication for the user such as "recommended to take a small amount" or "recommended to take a large amount" based on nutritional balance.

[1770] "Example 2: Combining Emotion Engines"

[1771] (Claim 1)

[1772] A photographing means for a user to photograph an image of food and drink;

[1773] a transmitting means for transmitting the captured image to a server;

[1774] an image analysis means for analyzing the transmitted image in the server and identifying the type and amount of food and drink;

[1775] a nutritional data acquisition means for acquiring nutritional component data of the identified food and drink by referring to a food database;

[1776] a biometric data acquisition means for acquiring biometric information of a pre-registered user;

[1777] a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional component data and biological information;

[1778] emotion analysis means for analyzing the emotional state of a user;

[1779] an advice generating means for generating dietary advice for a user based on the nutritional balance calculation result and the emotional state;

[1780] a notification means for transmitting the generated advice to a user's terminal;

[1781] A system including:

[1782] (Claim 2)

[1783] 2. The system of claim 1, further comprising a biometric data acquisition means, wherein the biometric information of the user includes the user's age, sex, health condition, and medical history.

[1784] (Claim 3)

[1785] 2. The system according to claim 1, wherein the advice generating means generates an index for recommending foods that have a relaxing effect or foods that help maintain the user's emotional state based on the nutritional balance calculation results and the user's emotional state.

[1786] "Application example 2 when combining emotion engines"

[1787] Claims

[1788] (Claim 1)

[1789] a terminal means for a user to take a photo of food and drink;

[1790] a transmission means for transmitting the photographed photo to a server;

[1791] an image analysis means in the server for analyzing the transmitted photograph and identifying the type and amount of food and drink;

[1792] a nutritional data acquisition means for acquiring nutritional component data of the identified food and drink by referring to a food database;

[1793] A healthcare data acquisition means for acquiring healthcare data of a pre-registered user;

[1794] a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional component data and health care data;

[1795] an emotion analysis means for customizing dietary advice based on the acquired emotion data;

[1796] an advice generation means for generating dietary advice for a user based on the calculation results and emotion data;

[1797] a notification means for transmitting the generated advice to a user's terminal;

[1798] A system including:

[1799] (Claim 2)

[1800] 10. The system of claim 1, further comprising a healthcare data acquisition means, wherein the user's healthcare data includes the user's age, gender, health condition, and medical history.

[1801] (Claim 3)

[1802] 2. The system according to claim 1, wherein the advice generating means generates an indication for the user such as "recommended to take a small amount" or "recommended to take a large amount" based on the nutritional balance and emotion data. [Explanation of symbols]

[1803] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a terminal means for a user to take a photo of food and drink; a transmission means for transmitting the photographed photo to a server; an image analysis means in the server for analyzing the transmitted photograph and identifying the type and amount of food and drink; a nutritional data acquisition means for acquiring nutritional component data of the identified food and drink by referring to a food database; A healthcare data acquisition means for acquiring healthcare data of a pre-registered user; a nutritional balance calculation means for calculating nutritional balance based on the acquired nutritional component data and health care data; an advice generating means for generating dietary advice for the user based on the calculation results; a notification means for transmitting the generated advice to a user's terminal; A system including:

2. The system of claim 1 , further comprising a healthcare data acquisition means, wherein the user's healthcare data includes the user's age, gender, health condition, and medical history.

3. 2. The system according to claim 1, wherein the advice generating means generates an indication for the user such as "recommended to take a small amount" or "recommended to take a large amount" based on nutritional balance.

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