system

Abstract

Provide a system. A means for a user to input physical data; means for transmitting the input physiological data to a server; a means for analyzing the received physiological data by the server and evaluating the health status of the user; a means for searching a food database for specific foods suitable for the health condition based on the assessment; a means of tailoring search results and generating specific food recommendations; means for transmitting the generated recommended food list to a user's terminal; A means for displaying the recommended food list received by the user's terminal; A system including:

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] In modern society, it is important to suggest appropriate dietary habits based on the health status of each individual user. However, current systems have difficulty providing specific food recommendations that match the user's health status. Furthermore, they are limited to abstract food categories (e.g., green and yellow vegetables) and are unable to present specific product names that users can actually purchase. This makes it difficult for users to properly select recommended foods, resulting in the problem of not being able to fully achieve the benefits of improving their health. [Means for solving the problem]

[0005] In order to solve the above problems, the following system is provided.

[0006] First, the system provides a means for users to input physical data such as weight, body temperature, and health checkup results. This data is sent to a server via the device. The server then analyzes the received physical data and evaluates the user's health condition. Based on the evaluated health condition, it identifies nutrients and ingredients suitable for maintaining or improving health. The server then references a food database to search for specific foods suitable for the user. The search results are adjusted taking into account the user's preferences and allergy information, and finally a specific list of recommended foods is generated. The generated list is sent to the device, and displayed on the user's device, allowing the user to easily select and purchase the recommended foods. This system enables users to receive specific food suggestions based on their individual health condition, which is expected to have an effect on improving their health.

[0007] "User" refers to an individual who utilizes the system to input physical data and receive appropriate food suggestions.

[0008] "Physical data" refers to information necessary to assess a user's health condition, such as weight, body temperature, and health checkup results.

[0009] A "terminal" refers to a device that allows a user to input data and transmit that data to a server. Examples include smartphones and wearable devices.

[0010] "Server" refers to the central system for receiving the physiological data sent by the user, performing analysis, and generating appropriate food suggestions.

[0011] "Analysis engine" refers to a software module that analyzes physical data within the server and evaluates the user's health condition.

[0012] A "food database" is a database that contains information on foods available on the market, including information on nutrients and ingredients.

[0013] "Food List" refers to a list of specific foods suggested to a user, generated by the analytics engine.

[0014] A "recommended food list" refers to a specific food suggestion list generated taking into account the user's preferences and allergy information.

[0015] "Preferences" refers to information about the types and characteristics of food and beverages that a user prefers.

[0016] "Allergy information" refers to information about foods or ingredients to which a user has an allergic reaction.

[0017] "Health status" refers to the current state of health and the presence or absence of disease, which is assessed based on the user's physical data. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention relates to a system in which a user inputs their own physical data, and a server analyzes the data and suggests specific foods suitable for the user's health condition. This system consists of three main components: the user, the terminal, and the server.

[0040] User data entry

[0041] First, the user uses a terminal such as a smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results. For example, the user enters their current weight and body temperature and the results of their most recent health checkup (e.g., blood sugar level, cholesterol level) into a form displayed on the application screen.

[0042] Sending data

[0043] The device collects the entered physical data and transmits it to the server, which receives the user's latest health information.

[0044] Data analysis by server

[0045] The server passes the received physical data to an analysis engine, which then specifically evaluates the user's health condition. Based on this evaluation, the necessary nutrients and ingredients are identified. For example, if the user is evaluated as being at risk of diabetes, nutrients and food ingredients that are effective in lowering blood sugar levels are recommended.

[0046] Food Database Reference

[0047] The server then searches a food database based on the evaluated nutrients and ingredients. The food database contains information on a variety of foods on the market, with detailed descriptions of each food's nutritional and ingredient information. It searches for specific product names (e.g., "low-sugar chocolate from a specific convenience store") and lists products suitable for suggesting to the user.

[0048] Generate a recommended food list

[0049] From the specific foods searched, the server generates a list of recommended foods, taking into account the user's preferences and allergy information. For example, foods containing ingredients to which the user is allergic will be excluded from the list, and conversely, favorite foods and frequently purchased foods will be prioritized.

[0050] Submit and view recommended food lists

[0051] The server sends the generated recommended food list to the terminal, which then displays the received list to the user. The displayed list includes specific product names, allowing the user to easily select and purchase these foods during everyday shopping.

[0052] Specific examples

[0053] For example, suppose User A weighs 70 kg, has a body temperature of 36.5 degrees, and has been diagnosed with "prediabetes" as a result of a health check. First, User A enters this data into an app on their smartphone, which then sends the data to the server. The server analyzes the received data and, based on User A's health condition, suggests foods that are effective in lowering blood sugar levels. After searching a food database, it determines that "low-carb chocolate from a specific convenience store" and "commercially available frozen low-carb pizza" are appropriate. The server compiles these products into a list and sends it to User A's smartphone. User A can then check the app, easily find the recommended foods, and purchase them.

[0054] The above is a specific embodiment for carrying out the present invention, which can support appropriate eating habits based on the health condition of each individual user, and is expected to have the effect of improving health.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] A user inputs physical data such as weight, body temperature, and health check results into an application on a smartphone or wearable device. This involves using the application's interface to type or select the required data into an input form.

[0058] Step 2:

[0059] The device collects the physiological data entered by the user and stores it locally, ready to be sent to a server later.

[0060] Step 3:

[0061] The device encrypts the collected body data and securely transmits it to a server using a secure communication protocol such as HTTPS.

[0062] Step 4:

[0063] The server receives the body data sent from the device and passes it to the analysis engine. First, it checks the integrity of the data and prepares it for passing to the analysis engine.

[0064] Step 5:

[0065] The server's analytics engine analyzes the received data and assesses the user's health status, which includes categorizing the user's condition based on pre-defined health indicators and ranges.

[0066] Step 6:

[0067] The server identifies the nutrients and ingredients needed based on the patient's health status, uses this information to reference a food database, and passes the identified nutrients and ingredients to the database as a search query.

[0068] Step 7:

[0069] The server searches a food database for specific foods suitable for the user's health condition, listing each food's nutritional content and characteristics, and summarizing the results.

[0070] Step 8:

[0071] The server generates a list of recommended foods from the search results, taking into account the user's preferences and allergy information. For example, if the user is allergic to a particular food, that food will be excluded from the list.

[0072] Step 9:

[0073] The server then re-encrypts the generated food recommendation list and securely transmits it to the device, where data is verified during the transmission process to ensure data integrity.

[0074] Step 10:

[0075] The terminal decodes the recommended food list received from the server and displays it to the user, who can then browse the recommended food list through the application and select suitable foods.

[0076] Step 11:

[0077] The user checks the recommended food list and selects the foods they want to purchase, allowing them to purchase specific foods recommended based on their own health condition.

[0078] Example 1

[0079] 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."

[0080] Many people today find it difficult to choose an appropriate diet based on their health condition. For those with specific health problems or allergies, finding the foods that are best for them can be time-consuming and laborious, potentially putting them at risk of health problems. Furthermore, proper nutritional management requires a system that analyzes various body data and suggests specific foods based on the results.

[0081] 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.

[0082] In this invention, the server includes means for a user to input physical data, means for transmitting the input physical data to the server, means for the server to analyze the received physical data and evaluate the user's health condition, means for searching a food database for specific foods suitable for the health condition based on the evaluation, means for adjusting the search results and generating a list of specific foods to recommend, means for transmitting the generated recommended food list to the user's terminal, means for displaying the recommended food list received by the user's terminal, and means for adjusting the recommended food list taking into account the user's preferences and allergy information, thereby enabling users to easily find foods suitable for their health condition.

[0083] "User" refers to a person who uses the system to input their own physical data and receive food suggestions based on that data.

[0084] "Terminal" refers to an electronic device used by a user to input physical data, and examples include smartphones and wearable devices.

[0085] "Server" refers to a central processing unit that receives and analyzes data sent by users.

[0086] "Physical data" refers to information that indicates the user's health condition, such as the user's weight, body temperature, and health check results.

[0087] "Analysis" refers to the process of evaluating the user's health condition based on the physical data received by the server and identifying the nutrients and ingredients needed.

[0088] "Evaluation" refers to the server analyzing the received physical data to determine the user's health condition.

[0089] A "food database" refers to a data store that contains information on the wide variety of foods available on the market.

[0090] "Recommended Food List" refers to a list of foods selected based on the user's health condition and preferences and allergy information.

[0091] "Preferences" refers to the food and taste preferences that a user particularly likes.

[0092] "Allergy information" refers to information about ingredients or foods that a user cannot consume.

[0093] This invention relates to a system in which a user inputs their own physical data, and a server analyzes the data and suggests specific foods suitable for the user's health condition. This system consists of three main components: the user, the terminal, and the server.

[0094] User data entry

[0095] Users use devices such as smartphones or wearable devices to input physical data such as weight, body temperature, and health checkup results. For example, a user might enter "weight 70 kg, body temperature 36.5 degrees, blood sugar level 110 mg / dL" into the smartphone app's input form. This allows the user's health condition to be recorded in detail and the data to be sent to the server.

[0096] Sending data

[0097] The device collects the entered physical data and sends it to the server. Specifically, after the user enters the data, the app sends the data to the server via the Internet by pressing the "Send" button. This process uses a secure communication protocol (e.g., HTTPS).

[0098] Data analysis by server

[0099] The server passes the received physical data to an analysis engine, which then specifically evaluates the user's health condition. Based on this evaluation, the necessary nutrients and ingredients are identified. The analysis uses data analysis scripts written in Python and machine learning models (e.g., Keras and TENSORFLOW (registered trademark)). For example, the server might evaluate that "your blood sugar level is high, so you need low-carbohydrate foods."

[0100] Food Database Reference

[0101] The server then searches a food database based on the evaluated nutrients and ingredients. The food database contains information on a variety of foods, with detailed descriptions of each food's nutritional and ingredient information. The server searches this database using SQL queries. For example, searching for foods using the keyword "low carb" can yield results such as "low carb chocolate" and "low carb pizza."

[0102] Generate a recommended food list

[0103] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. For example, if a user has a nut allergy, foods containing nuts will be excluded from the list. Conversely, foods that the user likes or frequently purchases will be prioritized in the list.

[0104] Submit and view recommended food lists

[0105] The server sends the generated recommended food list to the device, which then displays it to the user. Specifically, a list of low-carb foods is displayed on the smartphone app, allowing the user to easily select and purchase these foods. For example, the app screen displays lists of "low-carb chocolate" and "low-carb pizza."

[0106] Example prompt

[0107] "Design a system that will provide appropriate dietary suggestions for a person whose weight is 70 kg, whose body temperature is 36.5 degrees, and whose health checkup reveals that they have prediabetes."

[0108] In this way, this system can support optimal dietary habits for each individual user by suggesting foods based on the user's health status, contributing to the maintenance and improvement of health. In addition, the use of generative AI models enables personalized suggestions for each user, resulting in more accurate and effective food selection.

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1: User Data Entry

[0111] Users use a smartphone app or wearable device to input physical data such as weight, body temperature, and health checkup results. Specifically, they open the smartphone app and enter data such as "weight 70 kg, body temperature 36.5 degrees, blood sugar level 110 mg / dL" into the input form. This input data is stored in the device for use in subsequent processing.

[0112] Input: Physical data such as weight, body temperature, and health check results

[0113] Output: User's physical data stored in the device

[0114] Step 2: Sending data

[0115] The device collects the physiological data entered by the user and sends it to the server. When the user presses the "Send" button, the app sends the data to the server via the Internet using a secure communication protocol (e.g., HTTPS). The sent data is received by the server and prepared for analysis.

[0116] Input: User's physical data stored on the device

[0117] Output: User's physical data sent to the server

[0118] Step 3: Data analysis by the server

[0119] The server passes the received physical data to an analysis engine, which then specifically evaluates the user's health condition. The server analyzes the data using data analysis scripts written in Python and machine learning models (e.g., Keras and TensorFlow). If the server determines that the user's blood sugar level is high, it identifies the nutrients and ingredients needed (e.g., determining that "low-sugar foods are necessary").

[0120] Input: User's physical data sent to the server

[0121] Output: User's health status assessment (e.g. high blood sugar level)

[0122] Step 4: Look up the food database

[0123] The server searches the food database based on the evaluated nutrients and ingredients. The server uses SQL queries to search the database and retrieve a list of relevant foods. For example, the server searches the database for the keyword "low carb" and gets results such as "low carb chocolate" and "low carb pizza."

[0124] Input: User's health assessment results, food database

[0125] Output: A list of foods based on the evaluation results

[0126] Step 5: Generate a recommended food list

[0127] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. For example, if the user has a nut allergy, foods containing nuts will be excluded from the list. Conversely, foods that the user particularly likes will be included in the list with priority.

[0128] Input: A list of foods based on the evaluation results, user preferences and allergy information

[0129] Output: User-optimized food recommendation list

[0130] Step 6: Submit and view your recommended food list

[0131] The server sends the generated recommended food list to the device, which then displays it to the user. The list is displayed on the user's smartphone app, and the user can view specific product names and detailed information. For example, a list of "low-carb chocolate" or "low-carb pizza" is displayed, and the user can view links and information for purchasing those foods.

[0132] Input: Recommended Food List

[0133] Output: A list of recommended foods displayed on the user's device.

[0134] (Application example 1)

[0135] 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."

[0136] Many healthcare applications and systems today analyze a user's health status and recommend appropriate foods. However, these systems often lack a means for easily purchasing the recommended foods, making it difficult for users to actually obtain the suggested foods. Furthermore, there is a lack of a way to intuitively and conveniently display the list of recommended foods on a smartphone and link them to a purchase link. This reduces user convenience and limits the usefulness of the system.

[0137] 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.

[0138] In this invention, the server includes: means for a user to input physical data; means for transmitting the input physical data to the server; means for the server to analyze the received physical data and evaluate the user's health condition; means for searching a food database for specific foods suitable for the health condition based on the evaluation; means for adjusting the search results and generating a list of specific foods to recommend; means for transmitting the generated recommended food list to the user's terminal; means for displaying the recommended food list received by the user's terminal; means for providing the user with a link to directly purchase the recommended foods; and means for the terminal displaying the recommended food list to function as a smartphone. This allows the user to easily identify recommended foods based on their health condition and purchase them on the spot.

[0139] A "user" is someone who uses the system to input their physical data and receive a list of recommended foods.

[0140] "Physical data" is a general term for information that indicates the user's health condition, such as weight, body temperature, and health checkup results.

[0141] A "server" is a computer system that receives and analyzes the physiological data sent by the user.

[0142] "Analysis" is the process of evaluating the user's health condition based on the physical data received by the server and identifying the nutrients and ingredients needed.

[0143] A "food database" is a database that contains information on a variety of foods available on the market, with detailed information on the nutritional components and ingredients of each food.

[0144] A "recommended food list" is a list of specific foods that are suitable for the user's health condition.

[0145] "Device" refers to a device, such as a smartphone or other wearable device, through which a user inputs physical data and receives and displays a recommended food list.

[0146] A "purchase link" is a hyperlink that allows you to purchase the recommended food directly from an online shopping site.

[0147] A "smartphone" is a mobile phone that displays a list of recommended foods and provides links to purchase them.

[0148] This invention is a system in which a user inputs their own physical data, and a server analyzes that data and suggests specific foods suitable for the user's health condition. This system consists of three main components: the user, the terminal, and the server. Each component of this system and its function are described in detail below.

[0149] User data entry

[0150] First, the user uses a terminal such as a smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results. Specifically, the user enters their current weight and body temperature into the form displayed on the application screen, and also fills in the results of their most recent health checkup (e.g., blood sugar level, cholesterol level). This allows the user's latest health information to be recorded on the terminal.

[0151] Sending data

[0152] The device collects the inputted physiological data and transmits it to a server, using secure communication methods (e.g., HTTPS protocol) over the Internet to protect user privacy and data security.

[0153] Data analysis by server

[0154] The server passes the received physical data to an analysis engine (e.g., Python, TensorFlow) to specifically evaluate the user's health condition. Based on this evaluation, the necessary nutrients and ingredients are identified. For example, if the detected health problem is evaluated as "prediabetes," the server will identify nutrients and food ingredients that are effective in lowering blood sugar levels.

[0155] Food Database Reference

[0156] The server then searches a food database based on the evaluated nutrients and ingredients. The food database contains information on a wide variety of foods on the market, with detailed descriptions of each food's nutritional and ingredient information. The server identifies foods suitable for specific health conditions and lists the data for those foods.

[0157] Generate a recommended food list

[0158] The server generates a list of recommended foods from the searched specific foods, taking into account the user's preferences and allergy information. For example, it excludes foods that contain ingredients to which the user is allergic, and prioritizes favorite or frequently purchased foods.

[0159] Submit and view recommended food lists

[0160] The server sends the generated recommended food list to the terminal. The terminal displays the received list to the user. The displayed list includes specific product names and images, allowing the user to easily select and purchase these foods during everyday shopping. In addition, links to directly purchase the recommended foods are provided, allowing the user to easily purchase the foods using an online shopping site.

[0161] Specific examples

[0162] For example, suppose User A weighs 70 kg, has a body temperature of 36.5 degrees, and has been diagnosed with "prediabetes" as a result of a health check. First, User A enters this data into an app on their smartphone, and the device sends the data to the server. The server analyzes the received data and, based on User A's health condition, suggests foods that are effective in lowering blood sugar levels. After searching the food database, it determines that "low-carb chocolate" and "low-carb pizza" are appropriate. The server compiles these products into a list and sends it to User A's smartphone. User A can then check the app, easily find the recommended foods, and purchase them.

[0163] Example prompts for generative AI models

[0164] Create a Python program that suggests appropriate foods based on health data entered by the user (weight, body temperature, health checkup results, etc.). User data is sent via an API and analyzed on the server side. Specifically, the program needs a function to analyze the user data stored in the database and generate a list of foods appropriate for the user's health condition.

[0165] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0166] Step 1:

[0167] The user launches the smartphone application and inputs their physical data. Specifically, they enter data such as weight, body temperature, and health checkup results (e.g., blood glucose level, cholesterol level) into the application form. The input data is temporarily stored in the application. The input is the physical data manually provided by the user, and the output is the unsent physical data stored in the device. The device prepares to send this data to a server via the Internet.

[0168] Step 2:

[0169] The device sends the physiological data entered by the user to the server. A secure communication protocol (HTTPS) is used for transmission. The input is the user's physiological data collected in step 1, and the output is the user's physiological data sent to the server. Specifically, the sent data is stored in a database on the server.

[0170] Step 3:

[0171] The server passes the received physical data to an analysis engine (Python, TensorFlow, etc.) to evaluate the user's health condition. The input is the user's physical data stored on the server, and the output is the health assessment results from the analysis engine. This analysis may, for example, evaluate a user with high blood sugar levels as being at risk of diabetes. Specifically, the necessary nutrients and components are identified as health assessment results.

[0172] Step 4:

[0173] The server searches a food database based on the health assessment results. The inputs are the health assessment results and the food database, and the output is a list of specific foods suitable for the user's health condition. In this step, the nutritional and ingredient information of the foods is referenced to identify foods that are best suited to the user's health condition. Specifically, "low-carb chocolate" and "low-carb pizza" are searched for.

[0174] Step 5:

[0175] The server adjusts the search results and generates a specific list of recommended foods. The list is generated taking into account the user's preferences and allergy information. The input is the search results and the user's preferences and allergy information, and the output is an adjusted list of recommended foods. Specifically, a list is generated that excludes the user's favorite foods and ingredients to which they are allergic.

[0176] Step 6:

[0177] The server sends the generated recommended food list to the terminal. The input is the recommended food list, and the output is the recommended food list sent to the user's terminal, allowing the user to check the recommended foods.

[0178] Step 7:

[0179] The device displays the received recommended food list to the user. The displayed list includes specific product names, images, and purchase links. The input is the recommended food list sent from the server, and the output is the recommended food list displayed on the user's screen. Specifically, the user can tap on the list to directly access the online shopping site and purchase the recommended food.

[0180] Specific examples of operation

[0181] Let's say User A weighs 70 kg, has a body temperature of 36.5 degrees, and has been diagnosed with "prediabetes" as a result of a health check. User A enters this data into a smartphone app, and the device sends the data to a server. The server analyzes the data and identifies "low-carb chocolate" and "low-carb pizza" as recommended foods based on the user's health condition. A list containing these foods is sent to User A's smartphone and displayed in the app with a link to purchase them.

[0182] Example prompts for generative AI models

[0183] Create a Python program that suggests appropriate foods based on health data entered by the user (weight, body temperature, health checkup results, etc.). User data is sent via an API and analyzed on the server side. Specifically, the program needs a function to analyze the user data stored in the database and generate a list of foods appropriate for the user's health condition.

[0184] 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.

[0185] This invention relates to a system in which a user inputs their own physical data and emotions, and a server analyzes the data and suggests specific foods suitable for the user's health and emotional state. This system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0186] User data entry

[0187] First, the user uses a device such as a smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results. At the same time, the emotion engine recognizes the user's emotional state in real time. The emotion engine analyzes the user's facial expressions and voice to understand the user's current emotional state (e.g., stress, joy, sadness).

[0188] Sending data

[0189] The device collects the input body data and perceived emotional state, stores it locally, and then encrypts and transmits it to a server using a secure communication protocol such as HTTPS.

[0190] Data analysis by server

[0191] The server receives the physical and emotional data sent from the device and passes it to the analysis engine. First, it checks the integrity of the data and prepares it for passing to the analysis engine. The analysis engine analyzes the received data and evaluates the user's health and emotional state. This evaluation includes categorizing the user's condition based on pre-defined health indicators and ranges and emotional indicators.

[0192] Food Database Reference

[0193] The server then searches a food database based on the evaluated nutrients and ingredients, as well as the user's emotional state. The food database contains information on a variety of foods on the market, detailing each food's nutritional content and characteristics. It searches for specific product names (e.g., "low-sugar chocolate from a specific convenience store") and lists products suitable for suggesting to the user.

[0194] Generate a recommended food list

[0195] From the specific foods searched, the server generates a list of recommended foods, taking into account the user's preferences, allergy information, and even their emotional state. For example, if the user is feeling stressed, foods with a relaxing effect will be prioritized on the list. If the user is allergic to a particular food, that food will be excluded from the list.

[0196] Submit and view recommended food lists

[0197] The server re-encrypts the generated recommended food list and securely transmits it to the device. The data is verified during the transmission process to ensure data integrity. The device then displays the received list to the user. The user can view the recommended food list through the application and select suitable foods.

[0198] Specific examples

[0199] For example, suppose User B weighs 65 kg, has a body temperature of 36.7 degrees, has been diagnosed with "high blood pressure" as a result of a health check, and is currently feeling stressed. First, User B enters this data into an app on their smartphone, and the emotion engine recognizes their stress state. The device then sends this data to the server. The server analyzes the received data and, based on User B's health condition, suggests foods that are effective in lowering blood pressure and foods that help reduce stress. After searching the food database, it determines that "low-sodium soup from a specific convenience store" and "commercially available tea" are appropriate. The server then compiles these products into a list and sends it to User B's smartphone. User B can then check the app, easily find the recommended foods, and purchase them.

[0200] The above is a specific embodiment for carrying out the present invention, which can support appropriate eating habits based on the health and emotional state of each individual user, and is expected to have the effect of improving health.

[0201] The processing flow will be explained below.

[0202] Step 1:

[0203] Users use an application installed on their smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results, while the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state.

[0204] Step 2:

[0205] The device collects the physical data input by the user and the emotional data recognized by the emotion engine, and temporarily stores them locally.

[0206] Step 3:

[0207] The device encrypts the collected physical and emotional data and transmits it to a server using a secure communication channel (e.g., HTTPS).

[0208] Step 4:

[0209] The server receives the physical and emotional data sent from the device, first verifies the consistency and completeness of the data, and then prepares it for passing to the analysis engine.

[0210] Step 5:

[0211] The server's analysis engine analyzes the received data and evaluates the user's health and emotional state. This includes categorizing the user based on pre-defined health and emotional indicators. For example, if "high blood pressure" and "stress" are recognized simultaneously, the corresponding nutrients and ingredients will be identified.

[0212] Step 6:

[0213] The server then references a food database based on the assessed health and emotional state to identify suitable foods, for example, foods that lower blood pressure and reduce stress.

[0214] Step 7:

[0215] The server generates a list of recommended foods based on specific foods retrieved from a food database, and adjusts the list to take into account the user's preferences and allergies, such as foods the user particularly likes or foods containing ingredients that should be avoided.

[0216] Step 8:

[0217] The server re-encrypts the generated recommended food list and transmits it to the terminal using a secure communication channel.

[0218] Step 9:

[0219] The device decrypts the recommended food list received from the server and displays it to the user, including the specific product name, ingredient information, and where it can be purchased.

[0220] Step 10:

[0221] The user checks the recommended food list and selects the foods they actually want to purchase, allowing them to easily choose appropriate foods based on their own health and emotional state.

[0222] Specific examples

[0223] For example, suppose User B weighs 65 kg, has a body temperature of 36.7 degrees, has been diagnosed with high blood pressure as a result of a health check, and is currently feeling stressed. The process proceeds as follows:

[0224] 1. User B enters weight, body temperature, and health check results into a smartphone app.

[0225] 2. The emotion engine analyzes User B's facial expressions and voice and recognizes "stress."

[0226] 3. The device stores this data locally, encrypts it, and sends it to the server.

[0227] 4. The server receives the data, checks its integrity, and then analyzes it using the analysis engine.

[0228] 5. The analysis engine evaluates "high blood pressure" and "stress" and identifies the corresponding nutrients.

[0229] 6. The server searches a food database to identify foods that are low in sodium and have a relaxing effect.

[0230] 7. The server generates a list of recommended foods, including "low-sodium soup from a specific convenience store" and "commercially available tea."

[0231] 8. The server encrypts the list and sends it to the device.

[0232] 9. The device decrypts the list and displays it to User B.

[0233] 10. User B checks the app and purchases the recommended food.

[0234] In this way, the present invention provides specific food suggestions based on the user's health and emotional state, which can be expected to have an effect of improving health.

[0235] Example 2

[0236] 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."

[0237] Conventional systems evaluate a user's health status based solely on their physical data, without taking into account changes in their emotional state or stress levels. As a result, it is difficult to provide comprehensive health recommendations to users, and they may not be able to select the right foods. Furthermore, the system may not adequately reflect the user's preferences or allergy information, meaning that the suggested foods may not necessarily be optimal for the user.

[0238] 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.

[0239] In this invention, the server includes: means for a user to input physical data and emotional data; means for transmitting the input data to the server using a secure communication protocol; means for verifying the consistency of the physical data and emotional data received by the server; means for analyzing the verified data with an analysis engine and evaluating the user's health and emotional state; means for searching a food database for specific foods suitable for the user's health and emotional state based on the evaluation; means for adjusting the search results taking into account the user's preferences and allergy information and generating a recommended food list; means for encrypting the generated recommended food list and transmitting it to the user's terminal; and means for decrypting and displaying the recommended food list received by the user's terminal. This enables specific foods to be recommended taking into consideration the user's health and emotional state in a comprehensive manner, thereby providing optimal health management for the user.

[0240] "Physical data" refers to data that indicates the physical health condition of the user, such as weight, body temperature, and health checkup results.

[0241] "Emotion data" is data that indicates the psychological state of the user analyzed from their facial expressions and voice.

[0242] A "terminal" is a device used by a user to input data, and includes devices such as smartphones and wearable devices.

[0243] A "secure communication protocol" is a communication protocol that ensures security when transmitting data, and examples of this include HTTPS.

[0244] A "server" is a central processing unit that analyzes received data and generates a processing result.

[0245] "Integrity checking" is the process of verifying that received data is accurate and complete.

[0246] "Analysis Engine" means software or algorithms that assess the health and emotional state of a user based on the data received.

[0247] A "food database" is a data store that collects information about the nutritional components and characteristics of food.

[0248] A "recommended food list" is a list of foods generated by the server based on the user's health and emotional state.

[0249] "Encryption" is the process of transforming information to protect the data and make it difficult for third parties to understand.

[0250] "Decryption" is the process of restoring encrypted data to its original form.

[0251] This invention relates to a system in which a user inputs physical and emotional data, a server analyzes the data, and then suggests specific foods suitable for the user's health and emotional state. This system is composed of four main components: a user, a terminal, a server, and an emotion engine.

[0252] System Configuration

[0253] This system is realized using the following hardware and software.

[0254] Terminals used include smartphones and wearable devices (e.g., Apple Watch, Fitbit, etc.).

[0255] The server used is one with high-performance data analysis capabilities (e.g., Amazon Web Services, Microsoft® Azure®).

[0256] The emotion engine uses software (e.g., Microsoft Azure Face API, Google (registered trademark) Cloud Speech-to-Text) to recognize emotions from the user's facial expressions and voice.

[0257] The communication protocol used is HTTPS to ensure data security.

[0258] Specific processing

[0259] First, the user inputs physical data such as weight, body temperature, and health checkup results via a device such as a smartphone or wearable device. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data in real time. The data collected in this way is AES encrypted by the device and sent to the server using the HTTPS protocol.

[0260] The server then verifies the integrity of the securely received data. This process involves comparing hash values ​​to ensure data integrity. After integrity is verified, the data is passed to an analytics engine, which evaluates the user's health and emotional state. The analytics engine evaluates and categorizes the user's condition based on pre-defined health indicators (e.g., blood pressure, body temperature) and emotional indicators (e.g., stress level).

[0261] The server then uses this evaluation to search a food database, which contains detailed information about nutritional components and properties, to find specific foods that are suitable for the user's health and emotional state, such as "low-sodium foods" or "foods with a relaxing effect."

[0262] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. This list can be adjusted to exclude foods if the user has allergies to certain foods.

[0263] Finally, the generated recommended food list is encrypted again and sent to the device via HTTPS. The device decrypts the received list and displays it to the user. The user can then view the recommended food list through the application and select suitable foods.

[0264] Specific examples

[0265] For example, assume that User B weighs 65 kg, has a body temperature of 36.7 degrees, has been diagnosed with "high blood pressure" as a result of a health check, and is currently feeling stressed. User B enters this data into an app on his smartphone, and the emotion engine recognizes his stress state. The device sends this data to a server, which analyzes the received data. Based on the analysis results, the server searches a food database and suggests appropriate products such as "low-sodium soup from a specific convenience store" or "commercially available tea." The server then compiles these products into a list and sends it to User B's smartphone. User B can then check the app, easily find the recommended foods, and purchase them according to his health and emotional state.

[0266] Examples of prompt statements

[0267] "Please create a program for a system that evaluates the user's health condition based on 15 health indicators and suggests foods that are suitable for the results. The user will enter data through a smartphone app and the system will display the results. Please also provide specific code."

[0268] The above is a specific embodiment for carrying out the present invention, which makes it possible to provide appropriate health management based on the health and emotional state of each individual user.

[0269] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0270] Step 1:

[0271] The user inputs physical and emotional data into the device. Using a smartphone app, the user inputs physical data such as weight, body temperature, and health checkup results. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain their emotional state in real time. Based on this input data, the device temporarily stores the data. The input data is the user's weight of 65 kg, body temperature of 36.7 degrees, health checkup results of "high blood pressure," and emotional state of "stress." This obtains initial data on the user's health and emotional state.

[0272] Step 2:

[0273] The device sends the acquired data to the server. First, the input data is AES encrypted. Then, the encrypted data is sent to the server using the HTTPS protocol. This data transmission protects the user's privacy. For example, the following JSON format data is encrypted and sent: {"Weight": 65, "Body Temperature": 36.7, "Health Check Results": "High Blood Pressure", "Emotional State": "Stressed"}. This ensures that the data is sent to the server in a secure manner.

[0274] Step 3:

[0275] The server decrypts the encrypted data received from the device and checks its integrity. The server decrypts the data and checks the integrity of the data using a hash value. The server then converts the received data into the format required to pass it to the analysis engine. For example, after decryption, the following data is obtained: {"Weight": 65, "Body Temperature": 36.7, "Health Check Results": "High Blood Pressure", "Emotional State": "Stressed"}. This allows the server to obtain reliable data.

[0276] Step 4:

[0277] The server uses an analysis engine to evaluate the user's health and emotional state. Based on the received physical and emotional data, the analysis engine uses pre-set health and emotional indicators to categorize the user's condition. For example, it may be classified into the categories of "high blood pressure" and "high stress." Based on the analysis results, it can make appropriate dietary recommendations.

[0278] Step 5:

[0279] The server searches a food database based on the evaluation results. The server searches for food information suitable for the user's health and emotional state, such as "low-sodium foods" or "foods with a relaxing effect." For example, it searches for "low-sodium soups from a specific convenience store" or "commercially available tea." This search finds appropriate foods based on the user's condition.

[0280] Step 6:

[0281] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. For example, if the user has a "nut allergy," foods containing nuts will be excluded from the list. This generates a list of foods that are optimal for the user.

[0282] Step 7:

[0283] The server encrypts the generated recommended food list and sends it to the device via HTTPS. The server verifies the data before sending it to ensure the integrity of the list. The encrypted list is then sent to the device. This ensures that the recommended food list is delivered securely to the device.

[0284] Step 8:

[0285] The device decodes the received recommended food list and displays it to the user. The user can then view the decoded list through a smartphone app. This allows the user to easily check the suggested foods and make food choices based on their health and emotional state. For example, the list might include "low-sodium soup from a specific convenience store" and "commercially available tea."

[0286] Through these steps, the system can provide appropriate food recommendations based on the user's health and emotional state.

[0287] (Application example 2)

[0288] 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."

[0289] In modern society, there is a demand for comprehensive management of dietary habits based on health and emotional state. However, there is still no system that individually collects health data and emotional data, properly analyzes them, and recommends foods suitable for users. Furthermore, there is a lack of a means to quickly and easily purchase the recommended foods, which hinders user satisfaction. Therefore, there is a need for a system that comprehensively analyzes data related to health and emotional state, recommends personalized foods, and allows users to easily purchase them.

[0290] 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 means for the user to input physical data and emotional data, means for transmitting the input physical data and emotional data to the server, means for the server to analyze the received physical data and emotional data and evaluate the user's health condition and emotional state, means for searching a food database for specific foods suitable for the health condition and emotional state based on the evaluation, means for adjusting the search results and generating a list of specific foods to recommend, means for transmitting the generated recommended food list to the user's terminal, means for displaying the recommended food list received by the user's terminal, and means for including links to purchase the foods in the generated recommended food list. This makes it possible to individually suggest foods suitable for the user's health condition and emotional state and easily purchase them.

[0291] "User's physical data" refers to data related to the user's health condition, such as weight, body temperature, and health checkup results, entered by the user.

[0292] "Emotion data" is data that indicates the user's emotional state, such as stress, joy, sadness, etc.

[0293] "Means for sending to the server" refers to the technical means for transferring data entered from the user's terminal to the server via the Internet.

[0294] "Means for analyzing the physical and emotional data received by the server" refers to the technical means by which the server processes the data sent by the user and assesses their health and emotional state.

[0295] The "food database" is a database that organizes and stores information on various foods, such as their nutritional content, characteristics, and product names.

[0296] The "searching means" refers to the technical means by which the server searches for suitable foods from the food database based on the user's health and emotional state.

[0297] The "means for generating a specific food recommendation list" refers to a technical means by which the server creates a list of foods suitable for the user based on the search results.

[0298] The "means for displaying the recommended food list" refers to a technical means for displaying the recommended food list received by the user's terminal from the server in a form that is easy for the user to view.

[0299] "Means for including links to purchase food products" refers to technological means for including links to online shopping sites in the list of recommended food products so that users can purchase them directly.

[0300] This system uses the user's physical and emotional data to suggest specific foods suitable for the user's health and emotional state. The system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0301] Data Entry

[0302] Users use devices such as smartphones or wearable devices to input physical data such as weight, body temperature, and health checkup results. At the same time, the emotion engine recognizes the user's emotional state in real time. The emotion engine analyzes the user's facial expressions and voice to understand the user's current emotional state (e.g., stress, joy, sadness).

[0303] Sending data

[0304] The device collects the input body data and recognized emotional data, stores it locally, and then encrypts and transmits it to a server using a secure communication protocol such as HTTPS.

[0305] Data analysis by server

[0306] The server receives the physical and emotional data sent from the device and passes it to the analysis engine. The server first checks the integrity of the data and prepares it for passing to the analysis engine. The analysis engine analyzes the received data and evaluates the user's health and emotional state. This evaluation includes categorizing the user's condition based on pre-defined health indicators, ranges, and emotional indicators.

[0307] Food Database Reference

[0308] The server then searches a food database based on the assessed health and emotional state. The food database contains information on a variety of foods on the market, with detailed descriptions of each food's nutritional content and characteristics. It searches for specific product names (e.g., "low-sodium soup from a specific store") and lists products suitable for suggesting to the user.

[0309] Generate a recommended food list

[0310] The server generates a recommended food list from the specific foods searched, taking into account the user's preferences, allergy information, and even their emotional state. For example, if the user is feeling stressed, foods with a relaxing effect will be prioritized on the list. If the user is allergic to a particular food, that food will be excluded from the list. The recommended food list also includes links to purchase the food.

[0311] Submit and view recommended food lists

[0312] The server then re-encrypts the generated recommended food list and securely transmits it to the device. The data is verified during the transmission process to ensure data integrity. The device then displays the received list to the user. The user can then browse the recommended food list through the application, select suitable foods, and click the purchase link to purchase them on the online store.

[0313] Program processing explanation

[0314] A secure communication protocol (HTTPS) is used to send and receive data between the server and the device. Python and the Flask framework are used for data analysis. A database management system (e.g., MySQL (registered trademark)) is used to search the food database and generate lists. A machine learning library (e.g., TensorFlow) is used for the emotion engine to recognize the user's emotional state.

[0315] Examples of concrete examples and prompts

[0316] As a concrete example, suppose the user enters the following data:

[0317] Weight: 65kg

[0318] Body temperature: 36.7°C

[0319] Health checkup results: High blood pressure

[0320] Emotional state: Stress

[0321] Based on the data entered, the server will suggest "low-sodium soup from a specific store" or "commercially available tea" and display a link to purchase each.

[0322] Example prompt sentence:

[0323] Based on the data below, please suggest suitable foods for the user.

[0324] Weight: 65kg

[0325] Body temperature: 36.7°C

[0326] Health checkup results: High blood pressure

[0327] Emotional state: Stress

[0328] Please also include a purchase link for each of the suggested foods.

[0329] This allows users to easily find and purchase the foods that best suit their health and emotional state.

[0330] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0331] Step 1:

[0332] Users use smartphones or wearable devices to input their weight, body temperature, health check results, and emotional state. Emotional data is collected by the emotion engine by analyzing the user's facial expressions and voice in real time. The input data is stored on the device.

[0333] Inputs: weight, temperature, medical results, emotional state.

[0334] Output: Physical and emotional data stored on the device.

[0335] Step 2:

[0336] The device encrypts the stored physical and emotional data and transmits it to the server using the HTTPS protocol, ensuring the data is securely passed to the server.

[0337] Input: Physical and emotional data stored on the device.

[0338] Output: The encrypted data packet.

[0339] Step 3:

[0340] The server receives and decrypts the encrypted data sent from the terminal. The decrypted data undergoes a data integrity verification process and is then passed to the analysis engine.

[0341] Input: Encrypted data packet.

[0342] Output: Decoded physical and emotional data.

[0343] Step 4:

[0344] The server's analysis engine uses the received data to assess the user's health and emotional state. For example, it classifies the user's health status based on weight, body temperature, and medical checkup results, and classifies the user's emotional state, such as stress level, based on the emotional data.

[0345] Input: Decoded physical and emotional data.

[0346] Output: Health and emotional state assessment results.

[0347] Step 5:

[0348] The server searches a food database based on the user's assessed health and emotional state, including the nutritional content, characteristics, and product name of each food, and lists the foods that are best suited to the user's situation.

[0349] Input: Health and emotional state assessment results.

[0350] Output: The searched food list.

[0351] Step 6:

[0352] The server further adjusts the suggested food list, taking into account the user's preferences and allergy information, to generate a final recommended food list, which also includes a purchase link for each food item.

[0353] Input: Retrieved food list, user preferences and allergy information.

[0354] Output: A tailored food recommendation list.

[0355] Step 7:

[0356] The server encrypts the generated food recommendation list and sends it to the device, where data integrity is verified during the transmission process.

[0357] Input: Tailored recommended food list.

[0358] Output: Encrypted data packet.

[0359] Step 8:

[0360] The device receives the encrypted recommended food list and decrypts it. The decrypted list is displayed to the user through the application. The user can view the recommended food and click a purchase link to purchase the product on the online shopping site.

[0361] Input: Encrypted data packet.

[0362] Output: Decoded recommended food list.

[0363] 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.

[0364] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0365] 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.

[0366] [Second embodiment]

[0367] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0368] 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.

[0369] 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).

[0370] 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.

[0371] 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.

[0372] 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).

[0373] 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.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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."

[0379] This invention relates to a system in which a user inputs their own physical data, and a server analyzes the data and suggests specific foods suitable for the user's health condition. This system consists of three main components: the user, the terminal, and the server.

[0380] User data entry

[0381] First, the user uses a terminal such as a smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results. For example, the user enters their current weight and body temperature and the results of their most recent health checkup (e.g., blood sugar level, cholesterol level) into a form displayed on the application screen.

[0382] Sending data

[0383] The device collects the entered physical data and transmits it to the server, which receives the user's latest health information.

[0384] Data analysis by server

[0385] The server passes the received physical data to an analysis engine, which then specifically evaluates the user's health condition. Based on this evaluation, the necessary nutrients and ingredients are identified. For example, if the user is evaluated as being at risk of diabetes, nutrients and food ingredients that are effective in lowering blood sugar levels are recommended.

[0386] Food Database Reference

[0387] The server then searches a food database based on the evaluated nutrients and ingredients. The food database contains information on a variety of foods on the market, with detailed descriptions of each food's nutritional and ingredient information. It searches for specific product names (e.g., "low-sugar chocolate from a specific convenience store") and lists products suitable for suggesting to the user.

[0388] Generate a recommended food list

[0389] From the specific foods searched, the server generates a list of recommended foods, taking into account the user's preferences and allergy information. For example, foods containing ingredients to which the user is allergic will be excluded from the list, and conversely, favorite foods and frequently purchased foods will be prioritized.

[0390] Submit and view recommended food lists

[0391] The server sends the generated recommended food list to the terminal, which then displays the received list to the user. The displayed list includes specific product names, allowing the user to easily select and purchase these foods during everyday shopping.

[0392] Specific examples

[0393] For example, suppose User A weighs 70 kg, has a body temperature of 36.5 degrees, and has been diagnosed with "prediabetes" as a result of a health check. First, User A enters this data into an app on their smartphone, which then sends the data to the server. The server analyzes the received data and, based on User A's health condition, suggests foods that are effective in lowering blood sugar levels. After searching a food database, it determines that "low-carb chocolate from a specific convenience store" and "commercially available frozen low-carb pizza" are appropriate. The server compiles these products into a list and sends it to User A's smartphone. User A can then check the app, easily find the recommended foods, and purchase them.

[0394] The above is a specific embodiment for carrying out the present invention, which can support appropriate eating habits based on the health condition of each individual user, and is expected to have the effect of improving health.

[0395] The processing flow will be explained below.

[0396] Step 1:

[0397] A user inputs physical data such as weight, body temperature, and health check results into an application on a smartphone or wearable device. This involves using the application's interface to type or select the required data into an input form.

[0398] Step 2:

[0399] The device collects the physiological data entered by the user and stores it locally, ready to be sent to a server later.

[0400] Step 3:

[0401] The device encrypts the collected body data and securely transmits it to a server using a secure communication protocol such as HTTPS.

[0402] Step 4:

[0403] The server receives the body data sent from the device and passes it to the analysis engine. First, it checks the integrity of the data and prepares it for passing to the analysis engine.

[0404] Step 5:

[0405] The server's analytics engine analyzes the received data and assesses the user's health status, which includes categorizing the user's condition based on pre-defined health indicators and ranges.

[0406] Step 6:

[0407] The server identifies the nutrients and ingredients needed based on the patient's health status, uses this information to reference a food database, and passes the identified nutrients and ingredients to the database as a search query.

[0408] Step 7:

[0409] The server searches a food database for specific foods suitable for the user's health condition, listing each food's nutritional content and characteristics, and summarizing the results.

[0410] Step 8:

[0411] The server generates a list of recommended foods from the search results, taking into account the user's preferences and allergy information. For example, if the user is allergic to a particular food, that food will be excluded from the list.

[0412] Step 9:

[0413] The server then re-encrypts the generated food recommendation list and securely transmits it to the device, where data is verified during the transmission process to ensure data integrity.

[0414] Step 10:

[0415] The terminal decodes the recommended food list received from the server and displays it to the user, who can then browse the recommended food list through the application and select suitable foods.

[0416] Step 11:

[0417] The user checks the recommended food list and selects the foods they want to purchase, allowing them to purchase specific foods recommended based on their own health condition.

[0418] Example 1

[0419] 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."

[0420] Many people today find it difficult to choose an appropriate diet based on their health condition. For those with specific health problems or allergies, finding the foods that are best for them can be time-consuming and laborious, potentially putting them at risk of health problems. Furthermore, proper nutritional management requires a system that analyzes various body data and suggests specific foods based on the results.

[0421] 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.

[0422] In this invention, the server includes means for a user to input physical data, means for transmitting the input physical data to the server, means for the server to analyze the received physical data and evaluate the user's health condition, means for searching a food database for specific foods suitable for the health condition based on the evaluation, means for adjusting the search results and generating a list of specific foods to recommend, means for transmitting the generated recommended food list to the user's terminal, means for displaying the recommended food list received by the user's terminal, and means for adjusting the recommended food list taking into account the user's preferences and allergy information, thereby enabling users to easily find foods suitable for their health condition.

[0423] "User" refers to a person who uses the system to input their own physical data and receive food suggestions based on that data.

[0424] "Terminal" refers to an electronic device used by a user to input physical data, and examples include smartphones and wearable devices.

[0425] "Server" refers to a central processing unit that receives and analyzes data sent by users.

[0426] "Physical data" refers to information that indicates the user's health condition, such as the user's weight, body temperature, and health check results.

[0427] "Analysis" refers to the process of evaluating the user's health condition based on the physical data received by the server and identifying the nutrients and ingredients needed.

[0428] "Evaluation" refers to the server analyzing the received physical data to determine the user's health condition.

[0429] A "food database" refers to a data store that contains information on the wide variety of foods available on the market.

[0430] "Recommended Food List" refers to a list of foods selected based on the user's health condition and preferences and allergy information.

[0431] "Preferences" refers to the food and taste preferences that a user particularly likes.

[0432] "Allergy information" refers to information about ingredients or foods that a user cannot consume.

[0433] This invention relates to a system in which a user inputs their own physical data, and a server analyzes the data and suggests specific foods suitable for the user's health condition. This system consists of three main components: the user, the terminal, and the server.

[0434] User data entry

[0435] Users use devices such as smartphones or wearable devices to input physical data such as weight, body temperature, and health checkup results. For example, a user might enter "weight 70 kg, body temperature 36.5 degrees, blood sugar level 110 mg / dL" into the smartphone app's input form. This allows the user's health condition to be recorded in detail and the data to be sent to the server.

[0436] Sending data

[0437] The device collects the entered physical data and sends it to the server. Specifically, after the user enters the data, the app sends the data to the server via the Internet by pressing the "Send" button. This process uses a secure communication protocol (e.g., HTTPS).

[0438] Data analysis by server

[0439] The server passes the received physical data to an analysis engine, which then specifically evaluates the user's health condition. Based on this evaluation, the necessary nutrients and ingredients are identified. The analysis uses data analysis scripts written in Python and machine learning models (e.g., Keras and TensorFlow). For example, the server might evaluate that "your blood sugar level is high, so you need low-sugar foods."

[0440] Food Database Reference

[0441] The server then searches a food database based on the evaluated nutrients and ingredients. The food database contains information on a variety of foods, with detailed descriptions of each food's nutritional and ingredient information. The server searches this database using SQL queries. For example, searching for foods using the keyword "low carb" can yield results such as "low carb chocolate" and "low carb pizza."

[0442] Generate a recommended food list

[0443] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. For example, if a user has a nut allergy, foods containing nuts will be excluded from the list. Conversely, foods that the user likes or frequently purchases will be prioritized in the list.

[0444] Submit and view recommended food lists

[0445] The server sends the generated recommended food list to the device, which then displays it to the user. Specifically, a list of low-carb foods is displayed on the smartphone app, allowing the user to easily select and purchase these foods. For example, the app screen displays lists of "low-carb chocolate" and "low-carb pizza."

[0446] Example prompt

[0447] "Design a system that will provide appropriate dietary suggestions for a person whose weight is 70 kg, whose body temperature is 36.5 degrees, and whose health checkup reveals that they have prediabetes."

[0448] In this way, this system can support optimal dietary habits for each individual user by suggesting foods based on the user's health status, contributing to the maintenance and improvement of health. In addition, the use of generative AI models enables personalized suggestions for each user, resulting in more accurate and effective food selection.

[0449] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0450] Step 1: User Data Entry

[0451] Users use a smartphone app or wearable device to input physical data such as weight, body temperature, and health checkup results. Specifically, they open the smartphone app and enter data such as "weight 70 kg, body temperature 36.5 degrees, blood sugar level 110 mg / dL" into the input form. This input data is stored in the device for use in subsequent processing.

[0452] Input: Physical data such as weight, body temperature, and health check results

[0453] Output: User's physical data stored in the device

[0454] Step 2: Sending data

[0455] The device collects the physiological data entered by the user and sends it to the server. When the user presses the "Send" button, the app sends the data to the server via the Internet using a secure communication protocol (e.g., HTTPS). The sent data is received by the server and prepared for analysis.

[0456] Input: User's physical data stored on the device

[0457] Output: User's physical data sent to the server

[0458] Step 3: Data analysis by the server

[0459] The server passes the received physical data to an analysis engine, which then specifically evaluates the user's health condition. The server analyzes the data using data analysis scripts written in Python and machine learning models (e.g., Keras and TensorFlow). If the server determines that the user's blood sugar level is high, it identifies the nutrients and ingredients needed (e.g., determining that "low-sugar foods are necessary").

[0460] Input: User's physical data sent to the server

[0461] Output: User's health status assessment (e.g. high blood sugar level)

[0462] Step 4: Look up the food database

[0463] The server searches the food database based on the evaluated nutrients and ingredients. The server uses SQL queries to search the database and retrieve a list of relevant foods. For example, the server searches the database for the keyword "low carb" and gets results such as "low carb chocolate" and "low carb pizza."

[0464] Input: User's health assessment results, food database

[0465] Output: A list of foods based on the evaluation results

[0466] Step 5: Generate a recommended food list

[0467] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. For example, if the user has a nut allergy, foods containing nuts will be excluded from the list. Conversely, foods that the user particularly likes will be included in the list with priority.

[0468] Input: A list of foods based on the evaluation results, user preferences and allergy information

[0469] Output: User-optimized food recommendation list

[0470] Step 6: Submit and view your recommended food list

[0471] The server sends the generated recommended food list to the device, which then displays it to the user. The list is displayed on the user's smartphone app, and the user can view specific product names and detailed information. For example, a list of "low-carb chocolate" or "low-carb pizza" is displayed, and the user can view links and information for purchasing those foods.

[0472] Input: Recommended Food List

[0473] Output: A list of recommended foods displayed on the user's device.

[0474] (Application example 1)

[0475] 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."

[0476] Many healthcare applications and systems today analyze a user's health status and recommend appropriate foods. However, these systems often lack a means for easily purchasing the recommended foods, making it difficult for users to actually obtain the suggested foods. Furthermore, there is a lack of a way to intuitively and conveniently display the list of recommended foods on a smartphone and link them to a purchase link. This reduces user convenience and limits the usefulness of the system.

[0477] 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.

[0478] In this invention, the server includes: means for a user to input physical data; means for transmitting the input physical data to the server; means for the server to analyze the received physical data and evaluate the user's health condition; means for searching a food database for specific foods suitable for the health condition based on the evaluation; means for adjusting the search results and generating a list of specific foods to recommend; means for transmitting the generated recommended food list to the user's terminal; means for displaying the recommended food list received by the user's terminal; means for providing the user with a link to directly purchase the recommended foods; and means for the terminal displaying the recommended food list to function as a smartphone. This allows the user to easily identify recommended foods based on their health condition and purchase them on the spot.

[0479] A "user" is someone who uses the system to input their physical data and receive a list of recommended foods.

[0480] "Physical data" is a general term for information that indicates the user's health condition, such as weight, body temperature, and health checkup results.

[0481] A "server" is a computer system that receives and analyzes the physiological data sent by the user.

[0482] "Analysis" is the process of evaluating the user's health condition based on the physical data received by the server and identifying the nutrients and ingredients needed.

[0483] A "food database" is a database that contains information on a variety of foods available on the market, with detailed information on the nutritional components and ingredients of each food.

[0484] A "recommended food list" is a list of specific foods that are suitable for the user's health condition.

[0485] "Device" refers to a device, such as a smartphone or other wearable device, through which a user inputs physical data and receives and displays a recommended food list.

[0486] A "purchase link" is a hyperlink that allows you to purchase the recommended food directly from an online shopping site.

[0487] A "smartphone" is a mobile phone that displays a list of recommended foods and provides links to purchase them.

[0488] This invention is a system in which a user inputs their own physical data, and a server analyzes that data and suggests specific foods suitable for the user's health condition. This system consists of three main components: the user, the terminal, and the server. Each component of this system and its function are described in detail below.

[0489] User data entry

[0490] First, the user uses a terminal such as a smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results. Specifically, the user enters their current weight and body temperature into the form displayed on the application screen, and also fills in the results of their most recent health checkup (e.g., blood sugar level, cholesterol level). This allows the user's latest health information to be recorded on the terminal.

[0491] Sending data

[0492] The device collects the inputted physiological data and transmits it to a server, using secure communication methods (e.g., HTTPS protocol) over the Internet to protect user privacy and data security.

[0493] Data analysis by server

[0494] The server passes the received physical data to an analysis engine (e.g., Python, TensorFlow) to specifically evaluate the user's health condition. Based on this evaluation, the necessary nutrients and ingredients are identified. For example, if the detected health problem is evaluated as "prediabetes," the server will identify nutrients and food ingredients that are effective in lowering blood sugar levels.

[0495] Food Database Reference

[0496] The server then searches a food database based on the evaluated nutrients and ingredients. The food database contains information on a wide variety of foods on the market, with detailed descriptions of each food's nutritional and ingredient information. The server identifies foods suitable for specific health conditions and lists the data for those foods.

[0497] Generate a recommended food list

[0498] The server generates a list of recommended foods from the searched specific foods, taking into account the user's preferences and allergy information. For example, it excludes foods that contain ingredients to which the user is allergic, and prioritizes favorite or frequently purchased foods.

[0499] Submit and view recommended food lists

[0500] The server sends the generated recommended food list to the terminal. The terminal displays the received list to the user. The displayed list includes specific product names and images, allowing the user to easily select and purchase these foods during everyday shopping. In addition, links to directly purchase the recommended foods are provided, allowing the user to easily purchase the foods using an online shopping site.

[0501] Specific examples

[0502] For example, suppose User A weighs 70 kg, has a body temperature of 36.5 degrees, and has been diagnosed with "prediabetes" as a result of a health check. First, User A enters this data into an app on their smartphone, and the device sends the data to the server. The server analyzes the received data and, based on User A's health condition, suggests foods that are effective in lowering blood sugar levels. After searching the food database, it determines that "low-carb chocolate" and "low-carb pizza" are appropriate. The server compiles these products into a list and sends it to User A's smartphone. User A can then check the app, easily find the recommended foods, and purchase them.

[0503] Example prompts for generative AI models

[0504] Create a Python program that suggests appropriate foods based on health data entered by the user (weight, body temperature, health checkup results, etc.). User data is sent via an API and analyzed on the server side. Specifically, the program needs a function to analyze the user data stored in the database and generate a list of foods appropriate for the user's health condition.

[0505] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0506] Step 1:

[0507] The user launches the smartphone application and inputs their physical data. Specifically, they enter data such as weight, body temperature, and health checkup results (e.g., blood glucose level, cholesterol level) into the application form. The input data is temporarily stored in the application. The input is the physical data manually provided by the user, and the output is the unsent physical data stored in the device. The device prepares to send this data to a server via the Internet.

[0508] Step 2:

[0509] The device sends the physiological data entered by the user to the server. A secure communication protocol (HTTPS) is used for transmission. The input is the user's physiological data collected in step 1, and the output is the user's physiological data sent to the server. Specifically, the sent data is stored in a database on the server.

[0510] Step 3:

[0511] The server passes the received physical data to an analysis engine (Python, TensorFlow, etc.) to evaluate the user's health condition. The input is the user's physical data stored on the server, and the output is the health assessment results from the analysis engine. This analysis may, for example, evaluate a user with high blood sugar levels as being at risk of diabetes. Specifically, the necessary nutrients and components are identified as health assessment results.

[0512] Step 4:

[0513] The server searches a food database based on the health assessment results. The inputs are the health assessment results and the food database, and the output is a list of specific foods suitable for the user's health condition. In this step, the nutritional and ingredient information of the foods is referenced to identify foods that are best suited to the user's health condition. Specifically, "low-carb chocolate" and "low-carb pizza" are searched for.

[0514] Step 5:

[0515] The server adjusts the search results and generates a specific list of recommended foods. The list is generated taking into account the user's preferences and allergy information. The input is the search results and the user's preferences and allergy information, and the output is an adjusted list of recommended foods. Specifically, a list is generated that excludes the user's favorite foods and ingredients to which they are allergic.

[0516] Step 6:

[0517] The server sends the generated recommended food list to the terminal. The input is the recommended food list, and the output is the recommended food list sent to the user's terminal, allowing the user to check the recommended foods.

[0518] Step 7:

[0519] The device displays the received recommended food list to the user. The displayed list includes specific product names, images, and purchase links. The input is the recommended food list sent from the server, and the output is the recommended food list displayed on the user's screen. Specifically, the user can tap on the list to directly access the online shopping site and purchase the recommended food.

[0520] Specific examples of operation

[0521] Let's say User A weighs 70 kg, has a body temperature of 36.5 degrees, and has been diagnosed with "prediabetes" as a result of a health check. User A enters this data into a smartphone app, and the device sends the data to a server. The server analyzes the data and identifies "low-carb chocolate" and "low-carb pizza" as recommended foods based on the user's health condition. A list containing these foods is sent to User A's smartphone and displayed in the app with a link to purchase them.

[0522] Example prompts for generative AI models

[0523] Create a Python program that suggests appropriate foods based on health data entered by the user (weight, body temperature, health checkup results, etc.). User data is sent via an API and analyzed on the server side. Specifically, the program needs a function to analyze the user data stored in the database and generate a list of foods appropriate for the user's health condition.

[0524] 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.

[0525] This invention relates to a system in which a user inputs their own physical data and emotions, and a server analyzes the data and suggests specific foods suitable for the user's health and emotional state. This system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0526] User data entry

[0527] First, the user uses a device such as a smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results. At the same time, the emotion engine recognizes the user's emotional state in real time. The emotion engine analyzes the user's facial expressions and voice to understand the user's current emotional state (e.g., stress, joy, sadness).

[0528] Sending data

[0529] The device collects the input body data and perceived emotional state, stores it locally, and then encrypts and transmits it to a server using a secure communication protocol such as HTTPS.

[0530] Data analysis by server

[0531] The server receives the physical and emotional data sent from the device and passes it to the analysis engine. First, it checks the integrity of the data and prepares it for passing to the analysis engine. The analysis engine analyzes the received data and evaluates the user's health and emotional state. This evaluation includes categorizing the user's condition based on pre-defined health indicators and ranges and emotional indicators.

[0532] Food Database Reference

[0533] The server then searches a food database based on the evaluated nutrients and ingredients, as well as the user's emotional state. The food database contains information on a variety of foods on the market, detailing each food's nutritional content and characteristics. It searches for specific product names (e.g., "low-sugar chocolate from a specific convenience store") and lists products suitable for suggesting to the user.

[0534] Generate a recommended food list

[0535] From the specific foods searched, the server generates a list of recommended foods, taking into account the user's preferences, allergy information, and even their emotional state. For example, if the user is feeling stressed, foods with a relaxing effect will be prioritized on the list. If the user is allergic to a particular food, that food will be excluded from the list.

[0536] Submit and view recommended food lists

[0537] The server re-encrypts the generated recommended food list and securely transmits it to the device. The data is verified during the transmission process to ensure data integrity. The device then displays the received list to the user. The user can view the recommended food list through the application and select suitable foods.

[0538] Specific examples

[0539] For example, suppose User B weighs 65 kg, has a body temperature of 36.7 degrees, has been diagnosed with "high blood pressure" as a result of a health check, and is currently feeling stressed. First, User B enters this data into an app on their smartphone, and the emotion engine recognizes their stress state. The device then sends this data to the server. The server analyzes the received data and, based on User B's health condition, suggests foods that are effective in lowering blood pressure and foods that help reduce stress. After searching the food database, it determines that "low-sodium soup from a specific convenience store" and "commercially available tea" are appropriate. The server then compiles these products into a list and sends it to User B's smartphone. User B can then check the app, easily find the recommended foods, and purchase them.

[0540] The above is a specific embodiment for carrying out the present invention, which can support appropriate eating habits based on the health and emotional state of each individual user, and is expected to have the effect of improving health.

[0541] The processing flow will be explained below.

[0542] Step 1:

[0543] Users use an application installed on their smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results, while the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state.

[0544] Step 2:

[0545] The device collects the physical data input by the user and the emotional data recognized by the emotion engine, and temporarily stores them locally.

[0546] Step 3:

[0547] The device encrypts the collected physical and emotional data and transmits it to a server using a secure communication channel (e.g., HTTPS).

[0548] Step 4:

[0549] The server receives the physical and emotional data sent from the device, first verifies the consistency and completeness of the data, and then prepares it for passing to the analysis engine.

[0550] Step 5:

[0551] The server's analysis engine analyzes the received data and evaluates the user's health and emotional state. This includes categorizing the user based on pre-defined health and emotional indicators. For example, if "high blood pressure" and "stress" are recognized simultaneously, the corresponding nutrients and ingredients will be identified.

[0552] Step 6:

[0553] The server then references a food database based on the assessed health and emotional state to identify suitable foods, for example, foods that lower blood pressure and reduce stress.

[0554] Step 7:

[0555] The server generates a list of recommended foods based on specific foods retrieved from a food database, and adjusts the list to take into account the user's preferences and allergies, such as foods the user particularly likes or foods containing ingredients that should be avoided.

[0556] Step 8:

[0557] The server re-encrypts the generated recommended food list and transmits it to the terminal using a secure communication channel.

[0558] Step 9:

[0559] The device decrypts the recommended food list received from the server and displays it to the user, including the specific product name, ingredient information, and where it can be purchased.

[0560] Step 10:

[0561] The user checks the recommended food list and selects the foods they actually want to purchase, allowing them to easily choose appropriate foods based on their own health and emotional state.

[0562] Specific examples

[0563] For example, suppose User B weighs 65 kg, has a body temperature of 36.7 degrees, has been diagnosed with high blood pressure as a result of a health check, and is currently feeling stressed. The process proceeds as follows:

[0564] 1. User B enters weight, body temperature, and health check results into a smartphone app.

[0565] 2. The emotion engine analyzes User B's facial expressions and voice and recognizes "stress."

[0566] 3. The device stores this data locally, encrypts it, and sends it to the server.

[0567] 4. The server receives the data, checks its integrity, and then analyzes it using the analysis engine.

[0568] 5. The analysis engine evaluates "high blood pressure" and "stress" and identifies the corresponding nutrients.

[0569] 6. The server searches a food database to identify foods that are low in sodium and have a relaxing effect.

[0570] 7. The server generates a list of recommended foods, including "low-sodium soup from a specific convenience store" and "commercially available tea."

[0571] 8. The server encrypts the list and sends it to the device.

[0572] 9. The device decrypts the list and displays it to User B.

[0573] 10. User B checks the app and purchases the recommended food.

[0574] In this way, the present invention provides specific food suggestions based on the user's health and emotional state, which can be expected to have an effect of improving health.

[0575] Example 2

[0576] 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."

[0577] Conventional systems evaluate a user's health status based solely on their physical data, without taking into account changes in their emotional state or stress levels. As a result, it is difficult to provide comprehensive health recommendations to users, and they may not be able to select the right foods. Furthermore, the system may not adequately reflect the user's preferences or allergy information, meaning that the suggested foods may not necessarily be optimal for the user.

[0578] 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.

[0579] In this invention, the server includes: means for a user to input physical data and emotional data; means for transmitting the input data to the server using a secure communication protocol; means for verifying the consistency of the physical data and emotional data received by the server; means for analyzing the verified data with an analysis engine and evaluating the user's health and emotional state; means for searching a food database for specific foods suitable for the user's health and emotional state based on the evaluation; means for adjusting the search results taking into account the user's preferences and allergy information and generating a recommended food list; means for encrypting the generated recommended food list and transmitting it to the user's terminal; and means for decrypting and displaying the recommended food list received by the user's terminal. This enables specific foods to be recommended taking into consideration the user's health and emotional state in a comprehensive manner, thereby providing optimal health management for the user.

[0580] "Physical data" refers to data that indicates the physical health condition of the user, such as weight, body temperature, and health checkup results.

[0581] "Emotion data" is data that indicates the psychological state of the user analyzed from their facial expressions and voice.

[0582] A "terminal" is a device used by a user to input data, and includes devices such as smartphones and wearable devices.

[0583] A "secure communication protocol" is a communication protocol that ensures security when transmitting data, and examples of this include HTTPS.

[0584] A "server" is a central processing unit that analyzes received data and generates a processing result.

[0585] "Integrity checking" is the process of verifying that received data is accurate and complete.

[0586] "Analysis Engine" means software or algorithms that assess the health and emotional state of a user based on the data received.

[0587] A "food database" is a data store that collects information about the nutritional components and characteristics of food.

[0588] A "recommended food list" is a list of foods generated by the server based on the user's health and emotional state.

[0589] "Encryption" is the process of transforming information to protect the data and make it difficult for third parties to understand.

[0590] "Decryption" is the process of restoring encrypted data to its original form.

[0591] This invention relates to a system in which a user inputs physical and emotional data, a server analyzes the data, and then suggests specific foods suitable for the user's health and emotional state. This system is composed of four main components: a user, a terminal, a server, and an emotion engine.

[0592] System Configuration

[0593] This system is realized using the following hardware and software.

[0594] Terminals used include smartphones and wearable devices (e.g., Apple Watch, Fitbit, etc.).

[0595] The servers used are those with high-performance data analysis capabilities (e.g., Amazon Web Services, Microsoft Azure).

[0596] The emotion engine uses software (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text) to recognize emotions from the user's facial expressions and voice.

[0597] The communication protocol used is HTTPS to ensure data security.

[0598] Specific processing

[0599] First, the user inputs physical data such as weight, body temperature, and health checkup results via a device such as a smartphone or wearable device. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data in real time. The data collected in this way is AES encrypted by the device and sent to the server using the HTTPS protocol.

[0600] The server then verifies the integrity of the securely received data. This process involves comparing hash values ​​to ensure data integrity. After integrity is verified, the data is passed to an analytics engine, which evaluates the user's health and emotional state. The analytics engine evaluates and categorizes the user's condition based on pre-defined health indicators (e.g., blood pressure, body temperature) and emotional indicators (e.g., stress level).

[0601] The server then uses this evaluation to search a food database, which contains detailed information about nutritional components and properties, to find specific foods that are suitable for the user's health and emotional state, such as "low-sodium foods" or "foods with a relaxing effect."

[0602] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. This list can be adjusted to exclude foods if the user has allergies to certain foods.

[0603] Finally, the generated recommended food list is encrypted again and sent to the device via HTTPS. The device decrypts the received list and displays it to the user. The user can then view the recommended food list through the application and select suitable foods.

[0604] Specific examples

[0605] For example, assume that User B weighs 65 kg, has a body temperature of 36.7 degrees, has been diagnosed with "high blood pressure" as a result of a health check, and is currently feeling stressed. User B enters this data into an app on his smartphone, and the emotion engine recognizes his stress state. The device sends this data to a server, which analyzes the received data. Based on the analysis results, the server searches a food database and suggests appropriate products such as "low-sodium soup from a specific convenience store" or "commercially available tea." The server then compiles these products into a list and sends it to User B's smartphone. User B can then check the app, easily find the recommended foods, and purchase them according to his health and emotional state.

[0606] Examples of prompt statements

[0607] "Please create a program for a system that evaluates the user's health condition based on 15 health indicators and suggests foods that are suitable for the results. The user will enter data through a smartphone app and the system will display the results. Please also provide specific code."

[0608] The above is a specific embodiment for carrying out the present invention, which makes it possible to provide appropriate health management based on the health and emotional state of each individual user.

[0609] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0610] Step 1:

[0611] The user inputs physical and emotional data into the device. Using a smartphone app, the user inputs physical data such as weight, body temperature, and health checkup results. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain their emotional state in real time. Based on this input data, the device temporarily stores the data. The input data is the user's weight of 65 kg, body temperature of 36.7 degrees, health checkup results of "high blood pressure," and emotional state of "stress." This obtains initial data on the user's health and emotional state.

[0612] Step 2:

[0613] The device sends the acquired data to the server. First, the input data is AES encrypted. Then, the encrypted data is sent to the server using the HTTPS protocol. This data transmission protects the user's privacy. For example, the following JSON format data is encrypted and sent: {"Weight": 65, "Body Temperature": 36.7, "Health Check Results": "High Blood Pressure", "Emotional State": "Stressed"}. This ensures that the data is sent to the server in a secure manner.

[0614] Step 3:

[0615] The server decrypts the encrypted data received from the device and checks its integrity. The server decrypts the data and checks the integrity of the data using a hash value. The server then converts the received data into the format required to pass it to the analysis engine. For example, after decryption, the following data is obtained: {"Weight": 65, "Body Temperature": 36.7, "Health Check Results": "High Blood Pressure", "Emotional State": "Stressed"}. This allows the server to obtain reliable data.

[0616] Step 4:

[0617] The server uses an analysis engine to evaluate the user's health and emotional state. Based on the received physical and emotional data, the analysis engine uses pre-set health and emotional indicators to categorize the user's condition. For example, it may be classified into the categories of "high blood pressure" and "high stress." Based on the analysis results, it can make appropriate dietary recommendations.

[0618] Step 5:

[0619] The server searches a food database based on the evaluation results. The server searches for food information suitable for the user's health and emotional state, such as "low-sodium foods" or "foods with a relaxing effect." For example, it searches for "low-sodium soups from a specific convenience store" or "commercially available tea." This search finds appropriate foods based on the user's condition.

[0620] Step 6:

[0621] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. For example, if the user has a "nut allergy," foods containing nuts will be excluded from the list. This generates a list of foods that are optimal for the user.

[0622] Step 7:

[0623] The server encrypts the generated recommended food list and sends it to the device via HTTPS. The server verifies the data before sending it to ensure the integrity of the list. The encrypted list is then sent to the device. This ensures that the recommended food list is delivered securely to the device.

[0624] Step 8:

[0625] The device decodes the received recommended food list and displays it to the user. The user can then view the decoded list through a smartphone app. This allows the user to easily check the suggested foods and make food choices based on their health and emotional state. For example, the list might include "low-sodium soup from a specific convenience store" and "commercially available tea."

[0626] Through these steps, the system can provide appropriate food recommendations based on the user's health and emotional state.

[0627] (Application example 2)

[0628] 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."

[0629] In modern society, there is a demand for comprehensive management of dietary habits based on health and emotional state. However, there is still no system that individually collects health data and emotional data, properly analyzes them, and recommends foods suitable for users. Furthermore, there is a lack of a means to quickly and easily purchase the recommended foods, which hinders user satisfaction. Therefore, there is a need for a system that comprehensively analyzes data related to health and emotional state, recommends personalized foods, and allows users to easily purchase them.

[0630] 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 means for the user to input physical data and emotional data, means for transmitting the input physical data and emotional data to the server, means for the server to analyze the received physical data and emotional data and evaluate the user's health condition and emotional state, means for searching a food database for specific foods suitable for the health condition and emotional state based on the evaluation, means for adjusting the search results and generating a list of specific foods to recommend, means for transmitting the generated recommended food list to the user's terminal, means for displaying the recommended food list received by the user's terminal, and means for including links to purchase the foods in the generated recommended food list. This makes it possible to individually suggest foods suitable for the user's health condition and emotional state and easily purchase them.

[0631] "User's physical data" refers to data related to the user's health condition, such as weight, body temperature, and health checkup results, entered by the user.

[0632] "Emotion data" is data that indicates the user's emotional state, such as stress, joy, sadness, etc.

[0633] "Means for sending to the server" refers to the technical means for transferring data entered from the user's terminal to the server via the Internet.

[0634] "Means for analyzing the physical and emotional data received by the server" refers to the technical means by which the server processes the data sent by the user and assesses their health and emotional state.

[0635] The "food database" is a database that organizes and stores information on various foods, such as their nutritional content, characteristics, and product names.

[0636] The "searching means" refers to the technical means by which the server searches for suitable foods from the food database based on the user's health and emotional state.

[0637] The "means for generating a specific food recommendation list" refers to a technical means by which the server creates a list of foods suitable for the user based on the search results.

[0638] The "means for displaying the recommended food list" refers to a technical means for displaying the recommended food list received by the user's terminal from the server in a form that is easy for the user to view.

[0639] "Means for including links to purchase food products" refers to technological means for including links to online shopping sites in the list of recommended food products so that users can purchase them directly.

[0640] This system uses the user's physical and emotional data to suggest specific foods suitable for the user's health and emotional state. The system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0641] Data Entry

[0642] Users use devices such as smartphones or wearable devices to input physical data such as weight, body temperature, and health checkup results. At the same time, the emotion engine recognizes the user's emotional state in real time. The emotion engine analyzes the user's facial expressions and voice to understand the user's current emotional state (e.g., stress, joy, sadness).

[0643] Sending data

[0644] The device collects the input body data and recognized emotional data, stores it locally, and then encrypts and transmits it to a server using a secure communication protocol such as HTTPS.

[0645] Data analysis by server

[0646] The server receives the physical and emotional data sent from the device and passes it to the analysis engine. The server first checks the integrity of the data and prepares it for passing to the analysis engine. The analysis engine analyzes the received data and evaluates the user's health and emotional state. This evaluation includes categorizing the user's condition based on pre-defined health indicators, ranges, and emotional indicators.

[0647] Food Database Reference

[0648] The server then searches a food database based on the assessed health and emotional state. The food database contains information on a variety of foods on the market, with detailed descriptions of each food's nutritional content and characteristics. It searches for specific product names (e.g., "low-sodium soup from a specific store") and lists products suitable for suggesting to the user.

[0649] Generate a recommended food list

[0650] The server generates a recommended food list from the specific foods searched, taking into account the user's preferences, allergy information, and even their emotional state. For example, if the user is feeling stressed, foods with a relaxing effect will be prioritized on the list. If the user is allergic to a particular food, that food will be excluded from the list. The recommended food list also includes links to purchase the food.

[0651] Submit and view recommended food lists

[0652] The server then re-encrypts the generated recommended food list and securely transmits it to the device. The data is verified during the transmission process to ensure data integrity. The device then displays the received list to the user. The user can then browse the recommended food list through the application, select suitable foods, and click the purchase link to purchase them on the online store.

[0653] Program processing explanation

[0654] A secure communication protocol (HTTPS) is used to send and receive data between the server and the device. Python and the Flask framework are used for data analysis. A database management system (e.g., MySQL) is used to search the food database and generate lists. A machine learning library (e.g., TensorFlow) is used for the emotion engine to recognize the user's emotional state.

[0655] Examples of concrete examples and prompts

[0656] As a concrete example, suppose the user enters the following data:

[0657] Weight: 65kg

[0658] Body temperature: 36.7°C

[0659] Health checkup results: High blood pressure

[0660] Emotional state: Stress

[0661] Based on the data entered, the server will suggest "low-sodium soup from a specific store" or "commercially available tea" and display a link to purchase each.

[0662] Example prompt sentence:

[0663] Based on the data below, please suggest suitable foods for the user.

[0664] Weight: 65kg

[0665] Body temperature: 36.7°C

[0666] Health checkup results: High blood pressure

[0667] Emotional state: Stress

[0668] Please also include a purchase link for each of the suggested foods.

[0669] This allows users to easily find and purchase the foods that best suit their health and emotional state.

[0670] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0671] Step 1:

[0672] Users use smartphones or wearable devices to input their weight, body temperature, health check results, and emotional state. Emotional data is collected by the emotion engine by analyzing the user's facial expressions and voice in real time. The input data is stored on the device.

[0673] Inputs: weight, temperature, medical results, emotional state.

[0674] Output: Physical and emotional data stored on the device.

[0675] Step 2:

[0676] The device encrypts the stored physical and emotional data and transmits it to the server using the HTTPS protocol, ensuring the data is securely passed to the server.

[0677] Input: Physical and emotional data stored on the device.

[0678] Output: The encrypted data packet.

[0679] Step 3:

[0680] The server receives and decrypts the encrypted data sent from the terminal. The decrypted data undergoes a data integrity verification process and is then passed to the analysis engine.

[0681] Input: Encrypted data packet.

[0682] Output: Decoded physical and emotional data.

[0683] Step 4:

[0684] The server's analysis engine uses the received data to assess the user's health and emotional state. For example, it classifies the user's health status based on weight, body temperature, and medical checkup results, and classifies the user's emotional state, such as stress level, based on the emotional data.

[0685] Input: Decoded physical and emotional data.

[0686] Output: Health and emotional state assessment results.

[0687] Step 5:

[0688] The server searches a food database based on the user's assessed health and emotional state, including the nutritional content, characteristics, and product name of each food, and lists the foods that are best suited to the user's situation.

[0689] Input: Health and emotional state assessment results.

[0690] Output: The searched food list.

[0691] Step 6:

[0692] The server further adjusts the suggested food list, taking into account the user's preferences and allergy information, to generate a final recommended food list, which also includes a purchase link for each food item.

[0693] Input: Retrieved food list, user preferences and allergy information.

[0694] Output: A tailored food recommendation list.

[0695] Step 7:

[0696] The server encrypts the generated food recommendation list and sends it to the device, where data integrity is verified during the transmission process.

[0697] Input: Tailored recommended food list.

[0698] Output: Encrypted data packet.

[0699] Step 8:

[0700] The device receives the encrypted recommended food list and decrypts it. The decrypted list is displayed to the user through the application. The user can view the recommended food and click a purchase link to purchase the product on the online shopping site.

[0701] Input: Encrypted data packet.

[0702] Output: Decoded recommended food list.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] [Third embodiment]

[0707] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0708] 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.

[0709] 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).

[0710] 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.

[0711] 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.

[0712] 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).

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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."

[0719] This invention relates to a system in which a user inputs their own physical data, and a server analyzes the data and suggests specific foods suitable for the user's health condition. This system consists of three main components: the user, the terminal, and the server.

[0720] User data entry

[0721] First, the user uses a terminal such as a smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results. For example, the user enters their current weight and body temperature and the results of their most recent health checkup (e.g., blood sugar level, cholesterol level) into a form displayed on the application screen.

[0722] Sending data

[0723] The device collects the entered physical data and transmits it to the server, which receives the user's latest health information.

[0724] Data analysis by server

[0725] The server passes the received physical data to an analysis engine, which then specifically evaluates the user's health condition. Based on this evaluation, the necessary nutrients and ingredients are identified. For example, if the user is evaluated as being at risk of diabetes, nutrients and food ingredients that are effective in lowering blood sugar levels are recommended.

[0726] Food Database Reference

[0727] The server then searches a food database based on the evaluated nutrients and ingredients. The food database contains information on a variety of foods on the market, with detailed descriptions of each food's nutritional and ingredient information. It searches for specific product names (e.g., "low-sugar chocolate from a specific convenience store") and lists products suitable for suggesting to the user.

[0728] Generate a recommended food list

[0729] From the specific foods searched, the server generates a list of recommended foods, taking into account the user's preferences and allergy information. For example, foods containing ingredients to which the user is allergic will be excluded from the list, and conversely, favorite foods and frequently purchased foods will be prioritized.

[0730] Submit and view recommended food lists

[0731] The server sends the generated recommended food list to the terminal, which then displays the received list to the user. The displayed list includes specific product names, allowing the user to easily select and purchase these foods during everyday shopping.

[0732] Specific examples

[0733] For example, suppose User A weighs 70 kg, has a body temperature of 36.5 degrees, and has been diagnosed with "prediabetes" as a result of a health check. First, User A enters this data into an app on their smartphone, which then sends the data to the server. The server analyzes the received data and, based on User A's health condition, suggests foods that are effective in lowering blood sugar levels. After searching a food database, it determines that "low-carb chocolate from a specific convenience store" and "commercially available frozen low-carb pizza" are appropriate. The server compiles these products into a list and sends it to User A's smartphone. User A can then check the app, easily find the recommended foods, and purchase them.

[0734] The above is a specific embodiment for carrying out the present invention, which can support appropriate eating habits based on the health condition of each individual user, and is expected to have the effect of improving health.

[0735] The processing flow will be explained below.

[0736] Step 1:

[0737] A user inputs physical data such as weight, body temperature, and health check results into an application on a smartphone or wearable device. This involves using the application's interface to type or select the required data into an input form.

[0738] Step 2:

[0739] The device collects the physiological data entered by the user and stores it locally, ready to be sent to a server later.

[0740] Step 3:

[0741] The device encrypts the collected body data and securely transmits it to a server using a secure communication protocol such as HTTPS.

[0742] Step 4:

[0743] The server receives the body data sent from the device and passes it to the analysis engine. First, it checks the integrity of the data and prepares it for passing to the analysis engine.

[0744] Step 5:

[0745] The server's analytics engine analyzes the received data and assesses the user's health status, which includes categorizing the user's condition based on pre-defined health indicators and ranges.

[0746] Step 6:

[0747] The server identifies the nutrients and ingredients needed based on the patient's health status, uses this information to reference a food database, and passes the identified nutrients and ingredients to the database as a search query.

[0748] Step 7:

[0749] The server searches a food database for specific foods suitable for the user's health condition, listing each food's nutritional content and characteristics, and summarizing the results.

[0750] Step 8:

[0751] The server generates a list of recommended foods from the search results, taking into account the user's preferences and allergy information. For example, if the user is allergic to a particular food, that food will be excluded from the list.

[0752] Step 9:

[0753] The server then re-encrypts the generated food recommendation list and securely transmits it to the device, where data is verified during the transmission process to ensure data integrity.

[0754] Step 10:

[0755] The terminal decodes the recommended food list received from the server and displays it to the user, who can then browse the recommended food list through the application and select suitable foods.

[0756] Step 11:

[0757] The user checks the recommended food list and selects the foods they want to purchase, allowing them to purchase specific foods recommended based on their own health condition.

[0758] Example 1

[0759] 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."

[0760] Many people today find it difficult to choose an appropriate diet based on their health condition. For those with specific health problems or allergies, finding the foods that are best for them can be time-consuming and laborious, potentially putting them at risk of health problems. Furthermore, proper nutritional management requires a system that analyzes various body data and suggests specific foods based on the results.

[0761] 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.

[0762] In this invention, the server includes means for a user to input physical data, means for transmitting the input physical data to the server, means for the server to analyze the received physical data and evaluate the user's health condition, means for searching a food database for specific foods suitable for the health condition based on the evaluation, means for adjusting the search results and generating a list of specific foods to recommend, means for transmitting the generated recommended food list to the user's terminal, means for displaying the recommended food list received by the user's terminal, and means for adjusting the recommended food list taking into account the user's preferences and allergy information, thereby enabling users to easily find foods suitable for their health condition.

[0763] "User" refers to a person who uses the system to input their own physical data and receive food suggestions based on that data.

[0764] "Terminal" refers to an electronic device used by a user to input physical data, and examples include smartphones and wearable devices.

[0765] "Server" refers to a central processing unit that receives and analyzes data sent by users.

[0766] "Physical data" refers to information that indicates the user's health condition, such as the user's weight, body temperature, and health check results.

[0767] "Analysis" refers to the process of evaluating the user's health condition based on the physical data received by the server and identifying the nutrients and ingredients needed.

[0768] "Evaluation" refers to the server analyzing the received physical data to determine the user's health condition.

[0769] A "food database" refers to a data store that contains information on the wide variety of foods available on the market.

[0770] "Recommended Food List" refers to a list of foods selected based on the user's health condition and preferences and allergy information.

[0771] "Preferences" refers to the food and taste preferences that a user particularly likes.

[0772] "Allergy information" refers to information about ingredients or foods that a user cannot consume.

[0773] This invention relates to a system in which a user inputs their own physical data, and a server analyzes the data and suggests specific foods suitable for the user's health condition. This system consists of three main components: the user, the terminal, and the server.

[0774] User data entry

[0775] Users use devices such as smartphones or wearable devices to input physical data such as weight, body temperature, and health checkup results. For example, a user might enter "weight 70 kg, body temperature 36.5 degrees, blood sugar level 110 mg / dL" into the smartphone app's input form. This allows the user's health condition to be recorded in detail and the data to be sent to the server.

[0776] Sending data

[0777] The device collects the entered physical data and sends it to the server. Specifically, after the user enters the data, the app sends the data to the server via the Internet by pressing the "Send" button. This process uses a secure communication protocol (e.g., HTTPS).

[0778] Data analysis by server

[0779] The server passes the received physical data to an analysis engine, which then specifically evaluates the user's health condition. Based on this evaluation, the necessary nutrients and ingredients are identified. The analysis uses data analysis scripts written in Python and machine learning models (e.g., Keras and TensorFlow). For example, the server might evaluate that "your blood sugar level is high, so you need low-sugar foods."

[0780] Food Database Reference

[0781] The server then searches a food database based on the evaluated nutrients and ingredients. The food database contains information on a variety of foods, with detailed descriptions of each food's nutritional and ingredient information. The server searches this database using SQL queries. For example, searching for foods using the keyword "low carb" can yield results such as "low carb chocolate" and "low carb pizza."

[0782] Generate a recommended food list

[0783] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. For example, if a user has a nut allergy, foods containing nuts will be excluded from the list. Conversely, foods that the user likes or frequently purchases will be prioritized in the list.

[0784] Submit and view recommended food lists

[0785] The server sends the generated recommended food list to the device, which then displays it to the user. Specifically, a list of low-carb foods is displayed on the smartphone app, allowing the user to easily select and purchase these foods. For example, the app screen displays lists of "low-carb chocolate" and "low-carb pizza."

[0786] Example prompt

[0787] "Design a system that will provide appropriate dietary suggestions for a person whose weight is 70 kg, whose body temperature is 36.5 degrees, and whose health checkup reveals that they have prediabetes."

[0788] In this way, this system can support optimal dietary habits for each individual user by suggesting foods based on the user's health status, contributing to the maintenance and improvement of health. In addition, the use of generative AI models enables personalized suggestions for each user, resulting in more accurate and effective food selection.

[0789] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0790] Step 1: User Data Entry

[0791] Users use a smartphone app or wearable device to input physical data such as weight, body temperature, and health checkup results. Specifically, they open the smartphone app and enter data such as "weight 70 kg, body temperature 36.5 degrees, blood sugar level 110 mg / dL" into the input form. This input data is stored in the device for use in subsequent processing.

[0792] Input: Physical data such as weight, body temperature, and health check results

[0793] Output: User's physical data stored in the device

[0794] Step 2: Sending data

[0795] The device collects the physiological data entered by the user and sends it to the server. When the user presses the "Send" button, the app sends the data to the server via the Internet using a secure communication protocol (e.g., HTTPS). The sent data is received by the server and prepared for analysis.

[0796] Input: User's physical data stored on the device

[0797] Output: User's physical data sent to the server

[0798] Step 3: Data analysis by the server

[0799] The server passes the received physical data to an analysis engine, which then specifically evaluates the user's health condition. The server analyzes the data using data analysis scripts written in Python and machine learning models (e.g., Keras and TensorFlow). If the server determines that the user's blood sugar level is high, it identifies the nutrients and ingredients needed (e.g., determining that "low-sugar foods are necessary").

[0800] Input: User's physical data sent to the server

[0801] Output: User's health status assessment (e.g. high blood sugar level)

[0802] Step 4: Look up the food database

[0803] The server searches the food database based on the evaluated nutrients and ingredients. The server uses SQL queries to search the database and retrieve a list of relevant foods. For example, the server searches the database for the keyword "low carb" and gets results such as "low carb chocolate" and "low carb pizza."

[0804] Input: User's health assessment results, food database

[0805] Output: A list of foods based on the evaluation results

[0806] Step 5: Generate a recommended food list

[0807] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. For example, if the user has a nut allergy, foods containing nuts will be excluded from the list. Conversely, foods that the user particularly likes will be included in the list with priority.

[0808] Input: A list of foods based on the evaluation results, user preferences and allergy information

[0809] Output: User-optimized food recommendation list

[0810] Step 6: Submit and view your recommended food list

[0811] The server sends the generated recommended food list to the device, which then displays it to the user. The list is displayed on the user's smartphone app, and the user can view specific product names and detailed information. For example, a list of "low-carb chocolate" or "low-carb pizza" is displayed, and the user can view links and information for purchasing those foods.

[0812] Input: Recommended Food List

[0813] Output: A list of recommended foods displayed on the user's device.

[0814] (Application example 1)

[0815] 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."

[0816] Many healthcare applications and systems today analyze a user's health status and recommend appropriate foods. However, these systems often lack a means for easily purchasing the recommended foods, making it difficult for users to actually obtain the suggested foods. Furthermore, there is a lack of a way to intuitively and conveniently display the list of recommended foods on a smartphone and link them to a purchase link. This reduces user convenience and limits the usefulness of the system.

[0817] 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.

[0818] In this invention, the server includes: means for a user to input physical data; means for transmitting the input physical data to the server; means for the server to analyze the received physical data and evaluate the user's health condition; means for searching a food database for specific foods suitable for the health condition based on the evaluation; means for adjusting the search results and generating a list of specific foods to recommend; means for transmitting the generated recommended food list to the user's terminal; means for displaying the recommended food list received by the user's terminal; means for providing the user with a link to directly purchase the recommended foods; and means for the terminal displaying the recommended food list to function as a smartphone. This allows the user to easily identify recommended foods based on their health condition and purchase them on the spot.

[0819] A "user" is someone who uses the system to input their physical data and receive a list of recommended foods.

[0820] "Physical data" is a general term for information that indicates the user's health condition, such as weight, body temperature, and health checkup results.

[0821] A "server" is a computer system that receives and analyzes the physiological data sent by the user.

[0822] "Analysis" is the process of evaluating the user's health condition based on the physical data received by the server and identifying the nutrients and ingredients needed.

[0823] A "food database" is a database that contains information on a variety of foods available on the market, with detailed information on the nutritional components and ingredients of each food.

[0824] A "recommended food list" is a list of specific foods that are suitable for the user's health condition.

[0825] "Device" refers to a device, such as a smartphone or other wearable device, through which a user inputs physical data and receives and displays a recommended food list.

[0826] A "purchase link" is a hyperlink that allows you to purchase the recommended food directly from an online shopping site.

[0827] A "smartphone" is a mobile phone that displays a list of recommended foods and provides links to purchase them.

[0828] This invention is a system in which a user inputs their own physical data, and a server analyzes that data and suggests specific foods suitable for the user's health condition. This system consists of three main components: the user, the terminal, and the server. Each component of this system and its function are described in detail below.

[0829] User data entry

[0830] First, the user uses a terminal such as a smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results. Specifically, the user enters their current weight and body temperature into the form displayed on the application screen, and also fills in the results of their most recent health checkup (e.g., blood sugar level, cholesterol level). This allows the user's latest health information to be recorded on the terminal.

[0831] Sending data

[0832] The device collects the inputted physiological data and transmits it to a server, using secure communication methods (e.g., HTTPS protocol) over the Internet to protect user privacy and data security.

[0833] Data analysis by server

[0834] The server passes the received physical data to an analysis engine (e.g., Python, TensorFlow) to specifically evaluate the user's health condition. Based on this evaluation, the necessary nutrients and ingredients are identified. For example, if the detected health problem is evaluated as "prediabetes," the server will identify nutrients and food ingredients that are effective in lowering blood sugar levels.

[0835] Food Database Reference

[0836] The server then searches a food database based on the evaluated nutrients and ingredients. The food database contains information on a wide variety of foods on the market, with detailed descriptions of each food's nutritional and ingredient information. The server identifies foods suitable for specific health conditions and lists the data for those foods.

[0837] Generate a recommended food list

[0838] The server generates a list of recommended foods from the searched specific foods, taking into account the user's preferences and allergy information. For example, it excludes foods that contain ingredients to which the user is allergic, and prioritizes favorite or frequently purchased foods.

[0839] Submit and view recommended food lists

[0840] The server sends the generated recommended food list to the terminal. The terminal displays the received list to the user. The displayed list includes specific product names and images, allowing the user to easily select and purchase these foods during everyday shopping. In addition, links to directly purchase the recommended foods are provided, allowing the user to easily purchase the foods using an online shopping site.

[0841] Specific examples

[0842] For example, suppose User A weighs 70 kg, has a body temperature of 36.5 degrees, and has been diagnosed with "prediabetes" as a result of a health check. First, User A enters this data into an app on their smartphone, and the device sends the data to the server. The server analyzes the received data and, based on User A's health condition, suggests foods that are effective in lowering blood sugar levels. After searching the food database, it determines that "low-carb chocolate" and "low-carb pizza" are appropriate. The server compiles these products into a list and sends it to User A's smartphone. User A can then check the app, easily find the recommended foods, and purchase them.

[0843] Example prompts for generative AI models

[0844] Create a Python program that suggests appropriate foods based on health data entered by the user (weight, body temperature, health checkup results, etc.). User data is sent via an API and analyzed on the server side. Specifically, the program needs a function to analyze the user data stored in the database and generate a list of foods appropriate for the user's health condition.

[0845] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0846] Step 1:

[0847] The user launches the smartphone application and inputs their physical data. Specifically, they enter data such as weight, body temperature, and health checkup results (e.g., blood glucose level, cholesterol level) into the application form. The input data is temporarily stored in the application. The input is the physical data manually provided by the user, and the output is the unsent physical data stored in the device. The device prepares to send this data to a server via the Internet.

[0848] Step 2:

[0849] The device sends the physiological data entered by the user to the server. A secure communication protocol (HTTPS) is used for transmission. The input is the user's physiological data collected in step 1, and the output is the user's physiological data sent to the server. Specifically, the sent data is stored in a database on the server.

[0850] Step 3:

[0851] The server passes the received physical data to an analysis engine (Python, TensorFlow, etc.) to evaluate the user's health condition. The input is the user's physical data stored on the server, and the output is the health assessment results from the analysis engine. This analysis may, for example, evaluate a user with high blood sugar levels as being at risk of diabetes. Specifically, the necessary nutrients and components are identified as health assessment results.

[0852] Step 4:

[0853] The server searches a food database based on the health assessment results. The inputs are the health assessment results and the food database, and the output is a list of specific foods suitable for the user's health condition. In this step, the nutritional and ingredient information of the foods is referenced to identify foods that are best suited to the user's health condition. Specifically, "low-carb chocolate" and "low-carb pizza" are searched for.

[0854] Step 5:

[0855] The server adjusts the search results and generates a specific list of recommended foods. The list is generated taking into account the user's preferences and allergy information. The input is the search results and the user's preferences and allergy information, and the output is an adjusted list of recommended foods. Specifically, a list is generated that excludes the user's favorite foods and ingredients to which they are allergic.

[0856] Step 6:

[0857] The server sends the generated recommended food list to the terminal. The input is the recommended food list, and the output is the recommended food list sent to the user's terminal, allowing the user to check the recommended foods.

[0858] Step 7:

[0859] The device displays the received recommended food list to the user. The displayed list includes specific product names, images, and purchase links. The input is the recommended food list sent from the server, and the output is the recommended food list displayed on the user's screen. Specifically, the user can tap on the list to directly access the online shopping site and purchase the recommended food.

[0860] Specific examples of operation

[0861] Let's say User A weighs 70 kg, has a body temperature of 36.5 degrees, and has been diagnosed with "prediabetes" as a result of a health check. User A enters this data into a smartphone app, and the device sends the data to a server. The server analyzes the data and identifies "low-carb chocolate" and "low-carb pizza" as recommended foods based on the user's health condition. A list containing these foods is sent to User A's smartphone and displayed in the app with a link to purchase them.

[0862] Example prompts for generative AI models

[0863] Create a Python program that suggests appropriate foods based on health data entered by the user (weight, body temperature, health checkup results, etc.). User data is sent via an API and analyzed on the server side. Specifically, the program needs a function to analyze the user data stored in the database and generate a list of foods appropriate for the user's health condition.

[0864] 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.

[0865] This invention relates to a system in which a user inputs their own physical data and emotions, and a server analyzes the data and suggests specific foods suitable for the user's health and emotional state. This system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0866] User data entry

[0867] First, the user uses a device such as a smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results. At the same time, the emotion engine recognizes the user's emotional state in real time. The emotion engine analyzes the user's facial expressions and voice to understand the user's current emotional state (e.g., stress, joy, sadness).

[0868] Sending data

[0869] The device collects the input body data and perceived emotional state, stores it locally, and then encrypts and transmits it to a server using a secure communication protocol such as HTTPS.

[0870] Data analysis by server

[0871] The server receives the physical and emotional data sent from the device and passes it to the analysis engine. First, it checks the integrity of the data and prepares it for passing to the analysis engine. The analysis engine analyzes the received data and evaluates the user's health and emotional state. This evaluation includes categorizing the user's condition based on pre-defined health indicators and ranges and emotional indicators.

[0872] Food Database Reference

[0873] The server then searches a food database based on the evaluated nutrients and ingredients, as well as the user's emotional state. The food database contains information on a variety of foods on the market, detailing each food's nutritional content and characteristics. It searches for specific product names (e.g., "low-sugar chocolate from a specific convenience store") and lists products suitable for suggesting to the user.

[0874] Generate a recommended food list

[0875] From the specific foods searched, the server generates a list of recommended foods, taking into account the user's preferences, allergy information, and even their emotional state. For example, if the user is feeling stressed, foods with a relaxing effect will be prioritized on the list. If the user is allergic to a particular food, that food will be excluded from the list.

[0876] Submit and view recommended food lists

[0877] The server re-encrypts the generated recommended food list and securely transmits it to the device. The data is verified during the transmission process to ensure data integrity. The device then displays the received list to the user. The user can view the recommended food list through the application and select suitable foods.

[0878] Specific examples

[0879] For example, suppose User B weighs 65 kg, has a body temperature of 36.7 degrees, has been diagnosed with "high blood pressure" as a result of a health check, and is currently feeling stressed. First, User B enters this data into an app on their smartphone, and the emotion engine recognizes their stress state. The device then sends this data to the server. The server analyzes the received data and, based on User B's health condition, suggests foods that are effective in lowering blood pressure and foods that help reduce stress. After searching the food database, it determines that "low-sodium soup from a specific convenience store" and "commercially available tea" are appropriate. The server then compiles these products into a list and sends it to User B's smartphone. User B can then check the app, easily find the recommended foods, and purchase them.

[0880] The above is a specific embodiment for carrying out the present invention, which can support appropriate eating habits based on the health and emotional state of each individual user, and is expected to have the effect of improving health.

[0881] The processing flow will be explained below.

[0882] Step 1:

[0883] Users use an application installed on their smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results, while the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state.

[0884] Step 2:

[0885] The device collects the physical data input by the user and the emotional data recognized by the emotion engine, and temporarily stores them locally.

[0886] Step 3:

[0887] The device encrypts the collected physical and emotional data and transmits it to a server using a secure communication channel (e.g., HTTPS).

[0888] Step 4:

[0889] The server receives the physical and emotional data sent from the device, first verifies the consistency and completeness of the data, and then prepares it for passing to the analysis engine.

[0890] Step 5:

[0891] The server's analysis engine analyzes the received data and evaluates the user's health and emotional state. This includes categorizing the user based on pre-defined health and emotional indicators. For example, if "high blood pressure" and "stress" are recognized simultaneously, the corresponding nutrients and ingredients will be identified.

[0892] Step 6:

[0893] The server then references a food database based on the assessed health and emotional state to identify suitable foods, for example, foods that lower blood pressure and reduce stress.

[0894] Step 7:

[0895] The server generates a list of recommended foods based on specific foods retrieved from a food database, and adjusts the list to take into account the user's preferences and allergies, such as foods the user particularly likes or foods containing ingredients that should be avoided.

[0896] Step 8:

[0897] The server re-encrypts the generated recommended food list and transmits it to the terminal using a secure communication channel.

[0898] Step 9:

[0899] The device decrypts the recommended food list received from the server and displays it to the user, including the specific product name, ingredient information, and where it can be purchased.

[0900] Step 10:

[0901] The user checks the recommended food list and selects the foods they actually want to purchase, allowing them to easily choose appropriate foods based on their own health and emotional state.

[0902] Specific examples

[0903] For example, suppose User B weighs 65 kg, has a body temperature of 36.7 degrees, has been diagnosed with high blood pressure as a result of a health check, and is currently feeling stressed. The process proceeds as follows:

[0904] 1. User B enters weight, body temperature, and health check results into a smartphone app.

[0905] 2. The emotion engine analyzes User B's facial expressions and voice and recognizes "stress."

[0906] 3. The device stores this data locally, encrypts it, and sends it to the server.

[0907] 4. The server receives the data, checks its integrity, and then analyzes it using the analysis engine.

[0908] 5. The analysis engine evaluates "high blood pressure" and "stress" and identifies the corresponding nutrients.

[0909] 6. The server searches a food database to identify foods that are low in sodium and have a relaxing effect.

[0910] 7. The server generates a list of recommended foods, including "low-sodium soup from a specific convenience store" and "commercially available tea."

[0911] 8. The server encrypts the list and sends it to the device.

[0912] 9. The device decrypts the list and displays it to User B.

[0913] 10. User B checks the app and purchases the recommended food.

[0914] In this way, the present invention provides specific food suggestions based on the user's health and emotional state, which can be expected to have an effect of improving health.

[0915] Example 2

[0916] 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."

[0917] Conventional systems evaluate a user's health status based solely on their physical data, without taking into account changes in their emotional state or stress levels. As a result, it is difficult to provide comprehensive health recommendations to users, and they may not be able to select the right foods. Furthermore, the system may not adequately reflect the user's preferences or allergy information, meaning that the suggested foods may not necessarily be optimal for the user.

[0918] 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.

[0919] In this invention, the server includes: means for a user to input physical data and emotional data; means for transmitting the input data to the server using a secure communication protocol; means for verifying the consistency of the physical data and emotional data received by the server; means for analyzing the verified data with an analysis engine and evaluating the user's health and emotional state; means for searching a food database for specific foods suitable for the user's health and emotional state based on the evaluation; means for adjusting the search results taking into account the user's preferences and allergy information and generating a recommended food list; means for encrypting the generated recommended food list and transmitting it to the user's terminal; and means for decrypting and displaying the recommended food list received by the user's terminal. This enables specific foods to be recommended taking into consideration the user's health and emotional state in a comprehensive manner, thereby providing optimal health management for the user.

[0920] "Physical data" refers to data that indicates the physical health condition of the user, such as weight, body temperature, and health checkup results.

[0921] "Emotion data" is data that indicates the psychological state of the user analyzed from their facial expressions and voice.

[0922] A "terminal" is a device used by a user to input data, and includes devices such as smartphones and wearable devices.

[0923] A "secure communication protocol" is a communication protocol that ensures security when transmitting data, and examples of this include HTTPS.

[0924] A "server" is a central processing unit that analyzes received data and generates a processing result.

[0925] "Integrity checking" is the process of verifying that received data is accurate and complete.

[0926] "Analysis Engine" means software or algorithms that assess the health and emotional state of a user based on the data received.

[0927] A "food database" is a data store that collects information about the nutritional components and characteristics of food.

[0928] A "recommended food list" is a list of foods generated by the server based on the user's health and emotional state.

[0929] "Encryption" is the process of transforming information to protect the data and make it difficult for third parties to understand.

[0930] "Decryption" is the process of restoring encrypted data to its original form.

[0931] This invention relates to a system in which a user inputs physical and emotional data, a server analyzes the data, and then suggests specific foods suitable for the user's health and emotional state. This system is composed of four main components: a user, a terminal, a server, and an emotion engine.

[0932] System Configuration

[0933] This system is realized using the following hardware and software.

[0934] Terminals used include smartphones and wearable devices (e.g., Apple Watch, Fitbit, etc.).

[0935] The servers used are those with high-performance data analysis capabilities (e.g., Amazon Web Services, Microsoft Azure).

[0936] The emotion engine uses software (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text) to recognize emotions from the user's facial expressions and voice.

[0937] The communication protocol used is HTTPS to ensure data security.

[0938] Specific processing

[0939] First, the user inputs physical data such as weight, body temperature, and health checkup results via a device such as a smartphone or wearable device. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data in real time. The data collected in this way is AES encrypted by the device and sent to the server using the HTTPS protocol.

[0940] The server then verifies the integrity of the securely received data. This process involves comparing hash values ​​to ensure data integrity. After integrity is verified, the data is passed to an analytics engine, which evaluates the user's health and emotional state. The analytics engine evaluates and categorizes the user's condition based on pre-defined health indicators (e.g., blood pressure, body temperature) and emotional indicators (e.g., stress level).

[0941] The server then uses this evaluation to search a food database, which contains detailed information about nutritional components and properties, to find specific foods that are suitable for the user's health and emotional state, such as "low-sodium foods" or "foods with a relaxing effect."

[0942] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. This list can be adjusted to exclude foods if the user has allergies to certain foods.

[0943] Finally, the generated recommended food list is encrypted again and sent to the device via HTTPS. The device decrypts the received list and displays it to the user. The user can then view the recommended food list through the application and select suitable foods.

[0944] Specific examples

[0945] For example, assume that User B weighs 65 kg, has a body temperature of 36.7 degrees, has been diagnosed with "high blood pressure" as a result of a health check, and is currently feeling stressed. User B enters this data into an app on his smartphone, and the emotion engine recognizes his stress state. The device sends this data to a server, which analyzes the received data. Based on the analysis results, the server searches a food database and suggests appropriate products such as "low-sodium soup from a specific convenience store" or "commercially available tea." The server then compiles these products into a list and sends it to User B's smartphone. User B can then check the app, easily find the recommended foods, and purchase them according to his health and emotional state.

[0946] Examples of prompt statements

[0947] "Please create a program for a system that evaluates the user's health condition based on 15 health indicators and suggests foods that are suitable for the results. The user will enter data through a smartphone app and the system will display the results. Please also provide specific code."

[0948] The above is a specific embodiment for carrying out the present invention, which makes it possible to provide appropriate health management based on the health and emotional state of each individual user.

[0949] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0950] Step 1:

[0951] The user inputs physical and emotional data into the device. Using a smartphone app, the user inputs physical data such as weight, body temperature, and health checkup results. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain their emotional state in real time. Based on this input data, the device temporarily stores the data. The input data is the user's weight of 65 kg, body temperature of 36.7 degrees, health checkup results of "high blood pressure," and emotional state of "stress." This obtains initial data on the user's health and emotional state.

[0952] Step 2:

[0953] The device sends the acquired data to the server. First, the input data is AES encrypted. Then, the encrypted data is sent to the server using the HTTPS protocol. This data transmission protects the user's privacy. For example, the following JSON format data is encrypted and sent: {"Weight": 65, "Body Temperature": 36.7, "Health Check Results": "High Blood Pressure", "Emotional State": "Stressed"}. This ensures that the data is sent to the server in a secure manner.

[0954] Step 3:

[0955] The server decrypts the encrypted data received from the device and checks its integrity. The server decrypts the data and checks the integrity of the data using a hash value. The server then converts the received data into the format required to pass it to the analysis engine. For example, after decryption, the following data is obtained: {"Weight": 65, "Body Temperature": 36.7, "Health Check Results": "High Blood Pressure", "Emotional State": "Stressed"}. This allows the server to obtain reliable data.

[0956] Step 4:

[0957] The server uses an analysis engine to evaluate the user's health and emotional state. Based on the received physical and emotional data, the analysis engine uses pre-set health and emotional indicators to categorize the user's condition. For example, it may be classified into the categories of "high blood pressure" and "high stress." Based on the analysis results, it can make appropriate dietary recommendations.

[0958] Step 5:

[0959] The server searches a food database based on the evaluation results. The server searches for food information suitable for the user's health and emotional state, such as "low-sodium foods" or "foods with a relaxing effect." For example, it searches for "low-sodium soups from a specific convenience store" or "commercially available tea." This search finds appropriate foods based on the user's condition.

[0960] Step 6:

[0961] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. For example, if the user has a "nut allergy," foods containing nuts will be excluded from the list. This generates a list of foods that are optimal for the user.

[0962] Step 7:

[0963] The server encrypts the generated recommended food list and sends it to the device via HTTPS. The server verifies the data before sending it to ensure the integrity of the list. The encrypted list is then sent to the device. This ensures that the recommended food list is delivered securely to the device.

[0964] Step 8:

[0965] The device decodes the received recommended food list and displays it to the user. The user can then view the decoded list through a smartphone app. This allows the user to easily check the suggested foods and make food choices based on their health and emotional state. For example, the list might include "low-sodium soup from a specific convenience store" and "commercially available tea."

[0966] Through these steps, the system can provide appropriate food recommendations based on the user's health and emotional state.

[0967] (Application example 2)

[0968] 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."

[0969] In modern society, there is a demand for comprehensive management of dietary habits based on health and emotional state. However, there is still no system that individually collects health data and emotional data, properly analyzes them, and recommends foods suitable for users. Furthermore, there is a lack of a means to quickly and easily purchase the recommended foods, which hinders user satisfaction. Therefore, there is a need for a system that comprehensively analyzes data related to health and emotional state, recommends personalized foods, and allows users to easily purchase them.

[0970] 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 means for the user to input physical data and emotional data, means for transmitting the input physical data and emotional data to the server, means for the server to analyze the received physical data and emotional data and evaluate the user's health condition and emotional state, means for searching a food database for specific foods suitable for the health condition and emotional state based on the evaluation, means for adjusting the search results and generating a list of specific foods to recommend, means for transmitting the generated recommended food list to the user's terminal, means for displaying the recommended food list received by the user's terminal, and means for including links to purchase the foods in the generated recommended food list. This makes it possible to individually suggest foods suitable for the user's health condition and emotional state and easily purchase them.

[0971] "User's physical data" refers to data related to the user's health condition, such as weight, body temperature, and health checkup results, entered by the user.

[0972] "Emotion data" is data that indicates the user's emotional state, such as stress, joy, sadness, etc.

[0973] "Means for sending to the server" refers to the technical means for transferring data entered from the user's terminal to the server via the Internet.

[0974] "Means for analyzing the physical and emotional data received by the server" refers to the technical means by which the server processes the data sent by the user and assesses their health and emotional state.

[0975] The "food database" is a database that organizes and stores information on various foods, such as their nutritional content, characteristics, and product names.

[0976] The "searching means" refers to the technical means by which the server searches for suitable foods from the food database based on the user's health and emotional state.

[0977] The "means for generating a specific food recommendation list" refers to a technical means by which the server creates a list of foods suitable for the user based on the search results.

[0978] The "means for displaying the recommended food list" refers to a technical means for displaying the recommended food list received by the user's terminal from the server in a form that is easy for the user to view.

[0979] "Means for including links to purchase food products" refers to technological means for including links to online shopping sites in the list of recommended food products so that users can purchase them directly.

[0980] This system uses the user's physical and emotional data to suggest specific foods suitable for the user's health and emotional state. The system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0981] Data Entry

[0982] Users use devices such as smartphones or wearable devices to input physical data such as weight, body temperature, and health checkup results. At the same time, the emotion engine recognizes the user's emotional state in real time. The emotion engine analyzes the user's facial expressions and voice to understand the user's current emotional state (e.g., stress, joy, sadness).

[0983] Sending data

[0984] The device collects the input body data and recognized emotional data, stores it locally, and then encrypts and transmits it to a server using a secure communication protocol such as HTTPS.

[0985] Data analysis by server

[0986] The server receives the physical and emotional data sent from the device and passes it to the analysis engine. The server first checks the integrity of the data and prepares it for passing to the analysis engine. The analysis engine analyzes the received data and evaluates the user's health and emotional state. This evaluation includes categorizing the user's condition based on pre-defined health indicators, ranges, and emotional indicators.

[0987] Food Database Reference

[0988] The server then searches a food database based on the assessed health and emotional state. The food database contains information on a variety of foods on the market, with detailed descriptions of each food's nutritional content and characteristics. It searches for specific product names (e.g., "low-sodium soup from a specific store") and lists products suitable for suggesting to the user.

[0989] Generate a recommended food list

[0990] The server generates a recommended food list from the specific foods searched, taking into account the user's preferences, allergy information, and even their emotional state. For example, if the user is feeling stressed, foods with a relaxing effect will be prioritized on the list. If the user is allergic to a particular food, that food will be excluded from the list. The recommended food list also includes links to purchase the food.

[0991] Submit and view recommended food lists

[0992] The server then re-encrypts the generated recommended food list and securely transmits it to the device. The data is verified during the transmission process to ensure data integrity. The device then displays the received list to the user. The user can then browse the recommended food list through the application, select suitable foods, and click the purchase link to purchase them on the online store.

[0993] Program processing explanation

[0994] A secure communication protocol (HTTPS) is used to send and receive data between the server and the device. Python and the Flask framework are used for data analysis. A database management system (e.g., MySQL) is used to search the food database and generate lists. A machine learning library (e.g., TensorFlow) is used for the emotion engine to recognize the user's emotional state.

[0995] Examples of concrete examples and prompts

[0996] As a concrete example, suppose the user enters the following data:

[0997] Weight: 65kg

[0998] Body temperature: 36.7°C

[0999] Health checkup results: High blood pressure

[1000] Emotional state: Stress

[1001] Based on the data entered, the server will suggest "low-sodium soup from a specific store" or "commercially available tea" and display a link to purchase each.

[1002] Example prompt sentence:

[1003] Based on the data below, please suggest suitable foods for the user.

[1004] Weight: 65kg

[1005] Body temperature: 36.7°C

[1006] Health checkup results: High blood pressure

[1007] Emotional state: Stress

[1008] Please also include a purchase link for each of the suggested foods.

[1009] This allows users to easily find and purchase the foods that best suit their health and emotional state.

[1010] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1011] Step 1:

[1012] Users use smartphones or wearable devices to input their weight, body temperature, health check results, and emotional state. Emotional data is collected by the emotion engine by analyzing the user's facial expressions and voice in real time. The input data is stored on the device.

[1013] Inputs: weight, temperature, medical results, emotional state.

[1014] Output: Physical and emotional data stored on the device.

[1015] Step 2:

[1016] The device encrypts the stored physical and emotional data and transmits it to the server using the HTTPS protocol, ensuring the data is securely passed to the server.

[1017] Input: Physical and emotional data stored on the device.

[1018] Output: The encrypted data packet.

[1019] Step 3:

[1020] The server receives and decrypts the encrypted data sent from the terminal. The decrypted data undergoes a data integrity verification process and is then passed to the analysis engine.

[1021] Input: Encrypted data packet.

[1022] Output: Decoded physical and emotional data.

[1023] Step 4:

[1024] The server's analysis engine uses the received data to assess the user's health and emotional state. For example, it classifies the user's health status based on weight, body temperature, and medical checkup results, and classifies the user's emotional state, such as stress level, based on the emotional data.

[1025] Input: Decoded physical and emotional data.

[1026] Output: Health and emotional state assessment results.

[1027] Step 5:

[1028] The server searches a food database based on the user's assessed health and emotional state, including the nutritional content, characteristics, and product name of each food, and lists the foods that are best suited to the user's situation.

[1029] Input: Health and emotional state assessment results.

[1030] Output: The searched food list.

[1031] Step 6:

[1032] The server further adjusts the suggested food list, taking into account the user's preferences and allergy information, to generate a final recommended food list, which also includes a purchase link for each food item.

[1033] Input: Retrieved food list, user preferences and allergy information.

[1034] Output: A tailored food recommendation list.

[1035] Step 7:

[1036] The server encrypts the generated food recommendation list and sends it to the device, where data integrity is verified during the transmission process.

[1037] Input: Tailored recommended food list.

[1038] Output: Encrypted data packet.

[1039] Step 8:

[1040] The device receives the encrypted recommended food list and decrypts it. The decrypted list is displayed to the user through the application. The user can view the recommended food and click a purchase link to purchase the product on the online shopping site.

[1041] Input: Encrypted data packet.

[1042] Output: Decoded recommended food list.

[1043] 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.

[1044] 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.

[1045] 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.

[1046] [Fourth embodiment]

[1047] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1048] 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.

[1049] 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).

[1050] 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.

[1051] 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.

[1052] 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).

[1053] 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.

[1054] 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.

[1055] 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.

[1056] 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.

[1057] 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.

[1058] 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.

[1059] 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."

[1060] This invention relates to a system in which a user inputs their own physical data, and a server analyzes the data and suggests specific foods suitable for the user's health condition. This system consists of three main components: the user, the terminal, and the server.

[1061] User data entry

[1062] First, the user uses a terminal such as a smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results. For example, the user enters their current weight and body temperature and the results of their most recent health checkup (e.g., blood sugar level, cholesterol level) into a form displayed on the application screen.

[1063] Sending data

[1064] The device collects the entered physical data and transmits it to the server, which receives the user's latest health information.

[1065] Data analysis by server

[1066] The server passes the received physical data to an analysis engine, which then specifically evaluates the user's health condition. Based on this evaluation, the necessary nutrients and ingredients are identified. For example, if the user is evaluated as being at risk of diabetes, nutrients and food ingredients that are effective in lowering blood sugar levels are recommended.

[1067] Food Database Reference

[1068] The server then searches a food database based on the evaluated nutrients and ingredients. The food database contains information on a variety of foods on the market, with detailed descriptions of each food's nutritional and ingredient information. It searches for specific product names (e.g., "low-sugar chocolate from a specific convenience store") and lists products suitable for suggesting to the user.

[1069] Generate a recommended food list

[1070] From the specific foods searched, the server generates a list of recommended foods, taking into account the user's preferences and allergy information. For example, foods containing ingredients to which the user is allergic will be excluded from the list, and conversely, favorite foods and frequently purchased foods will be prioritized.

[1071] Submit and view recommended food lists

[1072] The server sends the generated recommended food list to the terminal, which then displays the received list to the user. The displayed list includes specific product names, allowing the user to easily select and purchase these foods during everyday shopping.

[1073] Specific examples

[1074] For example, suppose User A weighs 70 kg, has a body temperature of 36.5 degrees, and has been diagnosed with "prediabetes" as a result of a health check. First, User A enters this data into an app on their smartphone, which then sends the data to the server. The server analyzes the received data and, based on User A's health condition, suggests foods that are effective in lowering blood sugar levels. After searching a food database, it determines that "low-carb chocolate from a specific convenience store" and "commercially available frozen low-carb pizza" are appropriate. The server compiles these products into a list and sends it to User A's smartphone. User A can then check the app, easily find the recommended foods, and purchase them.

[1075] The above is a specific embodiment for carrying out the present invention, which can support appropriate eating habits based on the health condition of each individual user, and is expected to have the effect of improving health.

[1076] The processing flow will be explained below.

[1077] Step 1:

[1078] A user inputs physical data such as weight, body temperature, and health check results into an application on a smartphone or wearable device. This involves using the application's interface to type or select the required data into an input form.

[1079] Step 2:

[1080] The device collects the physiological data entered by the user and stores it locally, ready to be sent to a server later.

[1081] Step 3:

[1082] The device encrypts the collected body data and securely transmits it to a server using a secure communication protocol such as HTTPS.

[1083] Step 4:

[1084] The server receives the body data sent from the device and passes it to the analysis engine. First, it checks the integrity of the data and prepares it for passing to the analysis engine.

[1085] Step 5:

[1086] The server's analytics engine analyzes the received data and assesses the user's health status, which includes categorizing the user's condition based on pre-defined health indicators and ranges.

[1087] Step 6:

[1088] The server identifies the nutrients and ingredients needed based on the patient's health status, uses this information to reference a food database, and passes the identified nutrients and ingredients to the database as a search query.

[1089] Step 7:

[1090] The server searches a food database for specific foods suitable for the user's health condition, listing each food's nutritional content and characteristics, and summarizing the results.

[1091] Step 8:

[1092] The server generates a list of recommended foods from the search results, taking into account the user's preferences and allergy information. For example, if the user is allergic to a particular food, that food will be excluded from the list.

[1093] Step 9:

[1094] The server then re-encrypts the generated food recommendation list and securely transmits it to the device, where data is verified during the transmission process to ensure data integrity.

[1095] Step 10:

[1096] The terminal decodes the recommended food list received from the server and displays it to the user, who can then browse the recommended food list through the application and select suitable foods.

[1097] Step 11:

[1098] The user checks the recommended food list and selects the foods they want to purchase, allowing them to purchase specific foods recommended based on their own health condition.

[1099] Example 1

[1100] 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."

[1101] Many people today find it difficult to choose an appropriate diet based on their health condition. For those with specific health problems or allergies, finding the foods that are best for them can be time-consuming and laborious, potentially putting them at risk of health problems. Furthermore, proper nutritional management requires a system that analyzes various body data and suggests specific foods based on the results.

[1102] 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.

[1103] In this invention, the server includes means for a user to input physical data, means for transmitting the input physical data to the server, means for the server to analyze the received physical data and evaluate the user's health condition, means for searching a food database for specific foods suitable for the health condition based on the evaluation, means for adjusting the search results and generating a list of specific foods to recommend, means for transmitting the generated recommended food list to the user's terminal, means for displaying the recommended food list received by the user's terminal, and means for adjusting the recommended food list taking into account the user's preferences and allergy information, thereby enabling users to easily find foods suitable for their health condition.

[1104] "User" refers to a person who uses the system to input their own physical data and receive food suggestions based on that data.

[1105] "Terminal" refers to an electronic device used by a user to input physical data, and examples include smartphones and wearable devices.

[1106] "Server" refers to a central processing unit that receives and analyzes data sent by users.

[1107] "Physical data" refers to information that indicates the user's health condition, such as the user's weight, body temperature, and health check results.

[1108] "Analysis" refers to the process of evaluating the user's health condition based on the physical data received by the server and identifying the nutrients and ingredients needed.

[1109] "Evaluation" refers to the server analyzing the received physical data to determine the user's health condition.

[1110] A "food database" refers to a data store that contains information on the wide variety of foods available on the market.

[1111] "Recommended Food List" refers to a list of foods selected based on the user's health condition and preferences and allergy information.

[1112] "Preferences" refers to the food and taste preferences that a user particularly likes.

[1113] "Allergy information" refers to information about ingredients or foods that a user cannot consume.

[1114] This invention relates to a system in which a user inputs their own physical data, and a server analyzes the data and suggests specific foods suitable for the user's health condition. This system consists of three main components: the user, the terminal, and the server.

[1115] User data entry

[1116] Users use devices such as smartphones or wearable devices to input physical data such as weight, body temperature, and health checkup results. For example, a user might enter "weight 70 kg, body temperature 36.5 degrees, blood sugar level 110 mg / dL" into the smartphone app's input form. This allows the user's health condition to be recorded in detail and the data to be sent to the server.

[1117] Sending data

[1118] The device collects the entered physical data and sends it to the server. Specifically, after the user enters the data, the app sends the data to the server via the Internet by pressing the "Send" button. This process uses a secure communication protocol (e.g., HTTPS).

[1119] Data analysis by server

[1120] The server passes the received physical data to an analysis engine, which then specifically evaluates the user's health condition. Based on this evaluation, the necessary nutrients and ingredients are identified. The analysis uses data analysis scripts written in Python and machine learning models (e.g., Keras and TensorFlow). For example, the server might evaluate that "your blood sugar level is high, so you need low-sugar foods."

[1121] Food Database Reference

[1122] The server then searches a food database based on the evaluated nutrients and ingredients. The food database contains information on a variety of foods, with detailed descriptions of each food's nutritional and ingredient information. The server searches this database using SQL queries. For example, searching for foods using the keyword "low carb" can yield results such as "low carb chocolate" and "low carb pizza."

[1123] Generate a recommended food list

[1124] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. For example, if a user has a nut allergy, foods containing nuts will be excluded from the list. Conversely, foods that the user likes or frequently purchases will be prioritized in the list.

[1125] Submit and view recommended food lists

[1126] The server sends the generated recommended food list to the device, which then displays it to the user. Specifically, a list of low-carb foods is displayed on the smartphone app, allowing the user to easily select and purchase these foods. For example, the app screen displays lists of "low-carb chocolate" and "low-carb pizza."

[1127] Example prompt

[1128] "Design a system that will provide appropriate dietary suggestions for a person whose weight is 70 kg, whose body temperature is 36.5 degrees, and whose health checkup reveals that they have prediabetes."

[1129] In this way, this system can support optimal dietary habits for each individual user by suggesting foods based on the user's health status, contributing to the maintenance and improvement of health. In addition, the use of generative AI models enables personalized suggestions for each user, resulting in more accurate and effective food selection.

[1130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1131] Step 1: User Data Entry

[1132] Users use a smartphone app or wearable device to input physical data such as weight, body temperature, and health checkup results. Specifically, they open the smartphone app and enter data such as "weight 70 kg, body temperature 36.5 degrees, blood sugar level 110 mg / dL" into the input form. This input data is stored in the device for use in subsequent processing.

[1133] Input: Physical data such as weight, body temperature, and health check results

[1134] Output: User's physical data stored in the device

[1135] Step 2: Sending data

[1136] The device collects the physiological data entered by the user and sends it to the server. When the user presses the "Send" button, the app sends the data to the server via the Internet using a secure communication protocol (e.g., HTTPS). The sent data is received by the server and prepared for analysis.

[1137] Input: User's physical data stored on the device

[1138] Output: User's physical data sent to the server

[1139] Step 3: Data analysis by the server

[1140] The server passes the received physical data to an analysis engine, which then specifically evaluates the user's health condition. The server analyzes the data using data analysis scripts written in Python and machine learning models (e.g., Keras and TensorFlow). If the server determines that the user's blood sugar level is high, it identifies the nutrients and ingredients needed (e.g., determining that "low-sugar foods are necessary").

[1141] Input: User's physical data sent to the server

[1142] Output: User's health status assessment (e.g. high blood sugar level)

[1143] Step 4: Look up the food database

[1144] The server searches the food database based on the evaluated nutrients and ingredients. The server uses SQL queries to search the database and retrieve a list of relevant foods. For example, the server searches the database for the keyword "low carb" and gets results such as "low carb chocolate" and "low carb pizza."

[1145] Input: User's health assessment results, food database

[1146] Output: A list of foods based on the evaluation results

[1147] Step 5: Generate a recommended food list

[1148] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. For example, if the user has a nut allergy, foods containing nuts will be excluded from the list. Conversely, foods that the user particularly likes will be included in the list with priority.

[1149] Input: A list of foods based on the evaluation results, user preferences and allergy information

[1150] Output: User-optimized food recommendation list

[1151] Step 6: Submit and view your recommended food list

[1152] The server sends the generated recommended food list to the device, which then displays it to the user. The list is displayed on the user's smartphone app, and the user can view specific product names and detailed information. For example, a list of "low-carb chocolate" or "low-carb pizza" is displayed, and the user can view links and information for purchasing those foods.

[1153] Input: Recommended Food List

[1154] Output: A list of recommended foods displayed on the user's device.

[1155] (Application example 1)

[1156] 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."

[1157] Many healthcare applications and systems today analyze a user's health status and recommend appropriate foods. However, these systems often lack a means for easily purchasing the recommended foods, making it difficult for users to actually obtain the suggested foods. Furthermore, there is a lack of a way to intuitively and conveniently display the list of recommended foods on a smartphone and link them to a purchase link. This reduces user convenience and limits the usefulness of the system.

[1158] 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.

[1159] In this invention, the server includes: means for a user to input physical data; means for transmitting the input physical data to the server; means for the server to analyze the received physical data and evaluate the user's health condition; means for searching a food database for specific foods suitable for the health condition based on the evaluation; means for adjusting the search results and generating a list of specific foods to recommend; means for transmitting the generated recommended food list to the user's terminal; means for displaying the recommended food list received by the user's terminal; means for providing the user with a link to directly purchase the recommended foods; and means for the terminal displaying the recommended food list to function as a smartphone. This allows the user to easily identify recommended foods based on their health condition and purchase them on the spot.

[1160] A "user" is someone who uses the system to input their physical data and receive a list of recommended foods.

[1161] "Physical data" is a general term for information that indicates the user's health condition, such as weight, body temperature, and health checkup results.

[1162] A "server" is a computer system that receives and analyzes the physiological data sent by the user.

[1163] "Analysis" is the process of evaluating the user's health condition based on the physical data received by the server and identifying the nutrients and ingredients needed.

[1164] A "food database" is a database that contains information on a variety of foods available on the market, with detailed information on the nutritional components and ingredients of each food.

[1165] A "recommended food list" is a list of specific foods that are suitable for the user's health condition.

[1166] "Device" refers to a device, such as a smartphone or other wearable device, through which a user inputs physical data and receives and displays a recommended food list.

[1167] A "purchase link" is a hyperlink that allows you to purchase the recommended food directly from an online shopping site.

[1168] A "smartphone" is a mobile phone that displays a list of recommended foods and provides links to purchase them.

[1169] This invention is a system in which a user inputs their own physical data, and a server analyzes that data and suggests specific foods suitable for the user's health condition. This system consists of three main components: the user, the terminal, and the server. Each component of this system and its function are described in detail below.

[1170] User data entry

[1171] First, the user uses a terminal such as a smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results. Specifically, the user enters their current weight and body temperature into the form displayed on the application screen, and also fills in the results of their most recent health checkup (e.g., blood sugar level, cholesterol level). This allows the user's latest health information to be recorded on the terminal.

[1172] Sending data

[1173] The device collects the inputted physiological data and transmits it to a server, using secure communication methods (e.g., HTTPS protocol) over the Internet to protect user privacy and data security.

[1174] Data analysis by server

[1175] The server passes the received physical data to an analysis engine (e.g., Python, TensorFlow) to specifically evaluate the user's health condition. Based on this evaluation, the necessary nutrients and ingredients are identified. For example, if the detected health problem is evaluated as "prediabetes," the server will identify nutrients and food ingredients that are effective in lowering blood sugar levels.

[1176] Food Database Reference

[1177] The server then searches a food database based on the evaluated nutrients and ingredients. The food database contains information on a wide variety of foods on the market, with detailed descriptions of each food's nutritional and ingredient information. The server identifies foods suitable for specific health conditions and lists the data for those foods.

[1178] Generate a recommended food list

[1179] The server generates a list of recommended foods from the searched specific foods, taking into account the user's preferences and allergy information. For example, it excludes foods that contain ingredients to which the user is allergic, and prioritizes favorite or frequently purchased foods.

[1180] Submit and view recommended food lists

[1181] The server sends the generated recommended food list to the terminal. The terminal displays the received list to the user. The displayed list includes specific product names and images, allowing the user to easily select and purchase these foods during everyday shopping. In addition, links to directly purchase the recommended foods are provided, allowing the user to easily purchase the foods using an online shopping site.

[1182] Specific examples

[1183] For example, suppose User A weighs 70 kg, has a body temperature of 36.5 degrees, and has been diagnosed with "prediabetes" as a result of a health check. First, User A enters this data into an app on their smartphone, and the device sends the data to the server. The server analyzes the received data and, based on User A's health condition, suggests foods that are effective in lowering blood sugar levels. After searching the food database, it determines that "low-carb chocolate" and "low-carb pizza" are appropriate. The server compiles these products into a list and sends it to User A's smartphone. User A can then check the app, easily find the recommended foods, and purchase them.

[1184] Example prompts for generative AI models

[1185] Create a Python program that suggests appropriate foods based on health data entered by the user (weight, body temperature, health checkup results, etc.). User data is sent via an API and analyzed on the server side. Specifically, the program needs a function to analyze the user data stored in the database and generate a list of foods appropriate for the user's health condition.

[1186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1187] Step 1:

[1188] The user launches the smartphone application and inputs their physical data. Specifically, they enter data such as weight, body temperature, and health checkup results (e.g., blood glucose level, cholesterol level) into the application form. The input data is temporarily stored in the application. The input is the physical data manually provided by the user, and the output is the unsent physical data stored in the device. The device prepares to send this data to a server via the Internet.

[1189] Step 2:

[1190] The device sends the physiological data entered by the user to the server. A secure communication protocol (HTTPS) is used for transmission. The input is the user's physiological data collected in step 1, and the output is the user's physiological data sent to the server. Specifically, the sent data is stored in a database on the server.

[1191] Step 3:

[1192] The server passes the received physical data to an analysis engine (Python, TensorFlow, etc.) to evaluate the user's health condition. The input is the user's physical data stored on the server, and the output is the health assessment results from the analysis engine. This analysis may, for example, evaluate a user with high blood sugar levels as being at risk of diabetes. Specifically, the necessary nutrients and components are identified as health assessment results.

[1193] Step 4:

[1194] The server searches a food database based on the health assessment results. The inputs are the health assessment results and the food database, and the output is a list of specific foods suitable for the user's health condition. In this step, the nutritional and ingredient information of the foods is referenced to identify foods that are best suited to the user's health condition. Specifically, "low-carb chocolate" and "low-carb pizza" are searched for.

[1195] Step 5:

[1196] The server adjusts the search results and generates a specific list of recommended foods. The list is generated taking into account the user's preferences and allergy information. The input is the search results and the user's preferences and allergy information, and the output is an adjusted list of recommended foods. Specifically, a list is generated that excludes the user's favorite foods and ingredients to which they are allergic.

[1197] Step 6:

[1198] The server sends the generated recommended food list to the terminal. The input is the recommended food list, and the output is the recommended food list sent to the user's terminal, allowing the user to check the recommended foods.

[1199] Step 7:

[1200] The device displays the received recommended food list to the user. The displayed list includes specific product names, images, and purchase links. The input is the recommended food list sent from the server, and the output is the recommended food list displayed on the user's screen. Specifically, the user can tap on the list to directly access the online shopping site and purchase the recommended food.

[1201] Specific examples of operation

[1202] Let's say User A weighs 70 kg, has a body temperature of 36.5 degrees, and has been diagnosed with "prediabetes" as a result of a health check. User A enters this data into a smartphone app, and the device sends the data to a server. The server analyzes the data and identifies "low-carb chocolate" and "low-carb pizza" as recommended foods based on the user's health condition. A list containing these foods is sent to User A's smartphone and displayed in the app with a link to purchase them.

[1203] Example prompts for generative AI models

[1204] Create a Python program that suggests appropriate foods based on health data entered by the user (weight, body temperature, health checkup results, etc.). User data is sent via an API and analyzed on the server side. Specifically, the program needs a function to analyze the user data stored in the database and generate a list of foods appropriate for the user's health condition.

[1205] 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.

[1206] This invention relates to a system in which a user inputs their own physical data and emotions, and a server analyzes the data and suggests specific foods suitable for the user's health and emotional state. This system consists of four main components: the user, the terminal, the server, and the emotion engine.

[1207] User data entry

[1208] First, the user uses a device such as a smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results. At the same time, the emotion engine recognizes the user's emotional state in real time. The emotion engine analyzes the user's facial expressions and voice to understand the user's current emotional state (e.g., stress, joy, sadness).

[1209] Sending data

[1210] The device collects the input body data and perceived emotional state, stores it locally, and then encrypts and transmits it to a server using a secure communication protocol such as HTTPS.

[1211] Data analysis by server

[1212] The server receives the physical and emotional data sent from the device and passes it to the analysis engine. First, it checks the integrity of the data and prepares it for passing to the analysis engine. The analysis engine analyzes the received data and evaluates the user's health and emotional state. This evaluation includes categorizing the user's condition based on pre-defined health indicators and ranges and emotional indicators.

[1213] Food Database Reference

[1214] The server then searches a food database based on the evaluated nutrients and ingredients, as well as the user's emotional state. The food database contains information on a variety of foods on the market, detailing each food's nutritional content and characteristics. It searches for specific product names (e.g., "low-sugar chocolate from a specific convenience store") and lists products suitable for suggesting to the user.

[1215] Generate a recommended food list

[1216] From the specific foods searched, the server generates a list of recommended foods, taking into account the user's preferences, allergy information, and even their emotional state. For example, if the user is feeling stressed, foods with a relaxing effect will be prioritized on the list. If the user is allergic to a particular food, that food will be excluded from the list.

[1217] Submit and view recommended food lists

[1218] The server re-encrypts the generated recommended food list and securely transmits it to the device. The data is verified during the transmission process to ensure data integrity. The device then displays the received list to the user. The user can view the recommended food list through the application and select suitable foods.

[1219] Specific examples

[1220] For example, suppose User B weighs 65 kg, has a body temperature of 36.7 degrees, has been diagnosed with "high blood pressure" as a result of a health check, and is currently feeling stressed. First, User B enters this data into an app on their smartphone, and the emotion engine recognizes their stress state. The device then sends this data to the server. The server analyzes the received data and, based on User B's health condition, suggests foods that are effective in lowering blood pressure and foods that help reduce stress. After searching the food database, it determines that "low-sodium soup from a specific convenience store" and "commercially available tea" are appropriate. The server then compiles these products into a list and sends it to User B's smartphone. User B can then check the app, easily find the recommended foods, and purchase them.

[1221] The above is a specific embodiment for carrying out the present invention, which can support appropriate eating habits based on the health and emotional state of each individual user, and is expected to have the effect of improving health.

[1222] The processing flow will be explained below.

[1223] Step 1:

[1224] Users use an application installed on their smartphone or wearable device to input physical data such as weight, body temperature, and health checkup results, while the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state.

[1225] Step 2:

[1226] The device collects the physical data input by the user and the emotional data recognized by the emotion engine, and temporarily stores them locally.

[1227] Step 3:

[1228] The device encrypts the collected physical and emotional data and transmits it to a server using a secure communication channel (e.g., HTTPS).

[1229] Step 4:

[1230] The server receives the physical and emotional data sent from the device, first verifies the consistency and completeness of the data, and then prepares it for passing to the analysis engine.

[1231] Step 5:

[1232] The server's analysis engine analyzes the received data and evaluates the user's health and emotional state. This includes categorizing the user based on pre-defined health and emotional indicators. For example, if "high blood pressure" and "stress" are recognized simultaneously, the corresponding nutrients and ingredients will be identified.

[1233] Step 6:

[1234] The server then references a food database based on the assessed health and emotional state to identify suitable foods, for example, foods that lower blood pressure and reduce stress.

[1235] Step 7:

[1236] The server generates a list of recommended foods based on specific foods retrieved from a food database, and adjusts the list to take into account the user's preferences and allergies, such as foods the user particularly likes or foods containing ingredients that should be avoided.

[1237] Step 8:

[1238] The server re-encrypts the generated recommended food list and transmits it to the terminal using a secure communication channel.

[1239] Step 9:

[1240] The device decrypts the recommended food list received from the server and displays it to the user, including the specific product name, ingredient information, and where it can be purchased.

[1241] Step 10:

[1242] The user checks the recommended food list and selects the foods they actually want to purchase, allowing them to easily choose appropriate foods based on their own health and emotional state.

[1243] Specific examples

[1244] For example, suppose User B weighs 65 kg, has a body temperature of 36.7 degrees, has been diagnosed with high blood pressure as a result of a health check, and is currently feeling stressed. The process proceeds as follows:

[1245] 1. User B enters weight, body temperature, and health check results into a smartphone app.

[1246] 2. The emotion engine analyzes User B's facial expressions and voice and recognizes "stress."

[1247] 3. The device stores this data locally, encrypts it, and sends it to the server.

[1248] 4. The server receives the data, checks its integrity, and then analyzes it using the analysis engine.

[1249] 5. The analysis engine evaluates "high blood pressure" and "stress" and identifies the corresponding nutrients.

[1250] 6. The server searches a food database to identify foods that are low in sodium and have a relaxing effect.

[1251] 7. The server generates a list of recommended foods, including "low-sodium soup from a specific convenience store" and "commercially available tea."

[1252] 8. The server encrypts the list and sends it to the device.

[1253] 9. The device decrypts the list and displays it to User B.

[1254] 10. User B checks the app and purchases the recommended food.

[1255] In this way, the present invention provides specific food suggestions based on the user's health and emotional state, which can be expected to have an effect of improving health.

[1256] Example 2

[1257] 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."

[1258] Conventional systems evaluate a user's health status based solely on their physical data, without taking into account changes in their emotional state or stress levels. As a result, it is difficult to provide comprehensive health recommendations to users, and they may not be able to select the right foods. Furthermore, the system may not adequately reflect the user's preferences or allergy information, meaning that the suggested foods may not necessarily be optimal for the user.

[1259] 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.

[1260] In this invention, the server includes: means for a user to input physical data and emotional data; means for transmitting the input data to the server using a secure communication protocol; means for verifying the consistency of the physical data and emotional data received by the server; means for analyzing the verified data with an analysis engine and evaluating the user's health and emotional state; means for searching a food database for specific foods suitable for the user's health and emotional state based on the evaluation; means for adjusting the search results taking into account the user's preferences and allergy information and generating a recommended food list; means for encrypting the generated recommended food list and transmitting it to the user's terminal; and means for decrypting and displaying the recommended food list received by the user's terminal. This enables specific foods to be recommended taking into consideration the user's health and emotional state in a comprehensive manner, thereby providing optimal health management for the user.

[1261] "Physical data" refers to data that indicates the physical health condition of the user, such as weight, body temperature, and health checkup results.

[1262] "Emotion data" is data that indicates the psychological state of the user analyzed from their facial expressions and voice.

[1263] A "terminal" is a device used by a user to input data, and includes devices such as smartphones and wearable devices.

[1264] A "secure communication protocol" is a communication protocol that ensures security when transmitting data, and examples of this include HTTPS.

[1265] A "server" is a central processing unit that analyzes received data and generates a processing result.

[1266] "Integrity checking" is the process of verifying that received data is accurate and complete.

[1267] "Analysis Engine" means software or algorithms that assess the health and emotional state of a user based on the data received.

[1268] A "food database" is a data store that collects information about the nutritional components and characteristics of food.

[1269] A "recommended food list" is a list of foods generated by the server based on the user's health and emotional state.

[1270] "Encryption" is the process of transforming information to protect the data and make it difficult for third parties to understand.

[1271] "Decryption" is the process of restoring encrypted data to its original form.

[1272] This invention relates to a system in which a user inputs physical and emotional data, a server analyzes the data, and then suggests specific foods suitable for the user's health and emotional state. This system is composed of four main components: a user, a terminal, a server, and an emotion engine.

[1273] System Configuration

[1274] This system is realized using the following hardware and software.

[1275] Terminals used include smartphones and wearable devices (e.g., Apple Watch, Fitbit, etc.).

[1276] The servers used are those with high-performance data analysis capabilities (e.g., Amazon Web Services, Microsoft Azure).

[1277] The emotion engine uses software (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text) to recognize emotions from the user's facial expressions and voice.

[1278] The communication protocol used is HTTPS to ensure data security.

[1279] Specific processing

[1280] First, the user inputs physical data such as weight, body temperature, and health checkup results via a device such as a smartphone or wearable device. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data in real time. The data collected in this way is AES encrypted by the device and sent to the server using the HTTPS protocol.

[1281] The server then verifies the integrity of the securely received data. This process involves comparing hash values ​​to ensure data integrity. After integrity is verified, the data is passed to an analytics engine, which evaluates the user's health and emotional state. The analytics engine evaluates and categorizes the user's condition based on pre-defined health indicators (e.g., blood pressure, body temperature) and emotional indicators (e.g., stress level).

[1282] The server then uses this evaluation to search a food database, which contains detailed information about nutritional components and properties, to find specific foods that are suitable for the user's health and emotional state, such as "low-sodium foods" or "foods with a relaxing effect."

[1283] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. This list can be adjusted to exclude foods if the user has allergies to certain foods.

[1284] Finally, the generated recommended food list is encrypted again and sent to the device via HTTPS. The device decrypts the received list and displays it to the user. The user can then view the recommended food list through the application and select suitable foods.

[1285] Specific examples

[1286] For example, assume that User B weighs 65 kg, has a body temperature of 36.7 degrees, has been diagnosed with "high blood pressure" as a result of a health check, and is currently feeling stressed. User B enters this data into an app on his smartphone, and the emotion engine recognizes his stress state. The device sends this data to a server, which analyzes the received data. Based on the analysis results, the server searches a food database and suggests appropriate products such as "low-sodium soup from a specific convenience store" or "commercially available tea." The server then compiles these products into a list and sends it to User B's smartphone. User B can then check the app, easily find the recommended foods, and purchase them according to his health and emotional state.

[1287] Examples of prompt statements

[1288] "Please create a program for a system that evaluates the user's health condition based on 15 health indicators and suggests foods that are suitable for the results. The user will enter data through a smartphone app and the system will display the results. Please also provide specific code."

[1289] The above is a specific embodiment for carrying out the present invention, which makes it possible to provide appropriate health management based on the health and emotional state of each individual user.

[1290] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1291] Step 1:

[1292] The user inputs physical and emotional data into the device. Using a smartphone app, the user inputs physical data such as weight, body temperature, and health checkup results. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain their emotional state in real time. Based on this input data, the device temporarily stores the data. The input data is the user's weight of 65 kg, body temperature of 36.7 degrees, health checkup results of "high blood pressure," and emotional state of "stress." This obtains initial data on the user's health and emotional state.

[1293] Step 2:

[1294] The device sends the acquired data to the server. First, the input data is AES encrypted. Then, the encrypted data is sent to the server using the HTTPS protocol. This data transmission protects the user's privacy. For example, the following JSON format data is encrypted and sent: {"Weight": 65, "Body Temperature": 36.7, "Health Check Results": "High Blood Pressure", "Emotional State": "Stressed"}. This ensures that the data is sent to the server in a secure manner.

[1295] Step 3:

[1296] The server decrypts the encrypted data received from the device and checks its integrity. The server decrypts the data and checks the integrity of the data using a hash value. The server then converts the received data into the format required to pass it to the analysis engine. For example, after decryption, the following data is obtained: {"Weight": 65, "Body Temperature": 36.7, "Health Check Results": "High Blood Pressure", "Emotional State": "Stressed"}. This allows the server to obtain reliable data.

[1297] Step 4:

[1298] The server uses an analysis engine to evaluate the user's health and emotional state. Based on the received physical and emotional data, the analysis engine uses pre-set health and emotional indicators to categorize the user's condition. For example, it may be classified into the categories of "high blood pressure" and "high stress." Based on the analysis results, it can make appropriate dietary recommendations.

[1299] Step 5:

[1300] The server searches a food database based on the evaluation results. The server searches for food information suitable for the user's health and emotional state, such as "low-sodium foods" or "foods with a relaxing effect." For example, it searches for "low-sodium soups from a specific convenience store" or "commercially available tea." This search finds appropriate foods based on the user's condition.

[1301] Step 6:

[1302] The server generates a list of recommended foods based on the search results, taking into account the user's preferences and allergy information. For example, if the user has a "nut allergy," foods containing nuts will be excluded from the list. This generates a list of foods that are optimal for the user.

[1303] Step 7:

[1304] The server encrypts the generated recommended food list and sends it to the device via HTTPS. The server verifies the data before sending it to ensure the integrity of the list. The encrypted list is then sent to the device. This ensures that the recommended food list is delivered securely to the device.

[1305] Step 8:

[1306] The device decodes the received recommended food list and displays it to the user. The user can then view the decoded list through a smartphone app. This allows the user to easily check the suggested foods and make food choices based on their health and emotional state. For example, the list might include "low-sodium soup from a specific convenience store" and "commercially available tea."

[1307] Through these steps, the system can provide appropriate food recommendations based on the user's health and emotional state.

[1308] (Application example 2)

[1309] 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."

[1310] In modern society, there is a demand for comprehensive management of dietary habits based on health and emotional state. However, there is still no system that individually collects health data and emotional data, properly analyzes them, and recommends foods suitable for users. Furthermore, there is a lack of a means to quickly and easily purchase the recommended foods, which hinders user satisfaction. Therefore, there is a need for a system that comprehensively analyzes data related to health and emotional state, recommends personalized foods, and allows users to easily purchase them.

[1311] 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 means for the user to input physical data and emotional data, means for transmitting the input physical data and emotional data to the server, means for the server to analyze the received physical data and emotional data and evaluate the user's health condition and emotional state, means for searching a food database for specific foods suitable for the health condition and emotional state based on the evaluation, means for adjusting the search results and generating a list of specific foods to recommend, means for transmitting the generated recommended food list to the user's terminal, means for displaying the recommended food list received by the user's terminal, and means for including links to purchase the foods in the generated recommended food list. This makes it possible to individually suggest foods suitable for the user's health condition and emotional state and easily purchase them.

[1312] "User's physical data" refers to data related to the user's health condition, such as weight, body temperature, and health checkup results, entered by the user.

[1313] "Emotion data" is data that indicates the user's emotional state, such as stress, joy, sadness, etc.

[1314] "Means for sending to the server" refers to the technical means for transferring data entered from the user's terminal to the server via the Internet.

[1315] "Means for analyzing the physical and emotional data received by the server" refers to the technical means by which the server processes the data sent by the user and assesses their health and emotional state.

[1316] The "food database" is a database that organizes and stores information on various foods, such as their nutritional content, characteristics, and product names.

[1317] The "searching means" refers to the technical means by which the server searches for suitable foods from the food database based on the user's health and emotional state.

[1318] The "means for generating a specific food recommendation list" refers to a technical means by which the server creates a list of foods suitable for the user based on the search results.

[1319] The "means for displaying the recommended food list" refers to a technical means for displaying the recommended food list received by the user's terminal from the server in a form that is easy for the user to view.

[1320] "Means for including links to purchase food products" refers to technological means for including links to online shopping sites in the list of recommended food products so that users can purchase them directly.

[1321] This system uses the user's physical and emotional data to suggest specific foods suitable for the user's health and emotional state. The system consists of four main components: the user, the terminal, the server, and the emotion engine.

[1322] Data Entry

[1323] Users use devices such as smartphones or wearable devices to input physical data such as weight, body temperature, and health checkup results. At the same time, the emotion engine recognizes the user's emotional state in real time. The emotion engine analyzes the user's facial expressions and voice to understand the user's current emotional state (e.g., stress, joy, sadness).

[1324] Sending data

[1325] The device collects the input body data and recognized emotional data, stores it locally, and then encrypts and transmits it to a server using a secure communication protocol such as HTTPS.

[1326] Data analysis by server

[1327] The server receives the physical and emotional data sent from the device and passes it to the analysis engine. The server first checks the integrity of the data and prepares it for passing to the analysis engine. The analysis engine analyzes the received data and evaluates the user's health and emotional state. This evaluation includes categorizing the user's condition based on pre-defined health indicators, ranges, and emotional indicators.

[1328] Food Database Reference

[1329] The server then searches a food database based on the assessed health and emotional state. The food database contains information on a variety of foods on the market, with detailed descriptions of each food's nutritional content and characteristics. It searches for specific product names (e.g., "low-sodium soup from a specific store") and lists products suitable for suggesting to the user.

[1330] Generate a recommended food list

[1331] The server generates a recommended food list from the specific foods searched, taking into account the user's preferences, allergy information, and even their emotional state. For example, if the user is feeling stressed, foods with a relaxing effect will be prioritized on the list. If the user is allergic to a particular food, that food will be excluded from the list. The recommended food list also includes links to purchase the food.

[1332] Submit and view recommended food lists

[1333] The server then re-encrypts the generated recommended food list and securely transmits it to the device. The data is verified during the transmission process to ensure data integrity. The device then displays the received list to the user. The user can then browse the recommended food list through the application, select suitable foods, and click the purchase link to purchase them on the online store.

[1334] Program processing explanation

[1335] A secure communication protocol (HTTPS) is used to send and receive data between the server and the device. Python and the Flask framework are used for data analysis. A database management system (e.g., MySQL) is used to search the food database and generate lists. A machine learning library (e.g., TensorFlow) is used for the emotion engine to recognize the user's emotional state.

[1336] Examples of concrete examples and prompts

[1337] As a concrete example, suppose the user enters the following data:

[1338] Weight: 65kg

[1339] Body temperature: 36.7°C

[1340] Health checkup results: High blood pressure

[1341] Emotional state: Stress

[1342] Based on the data entered, the server will suggest "low-sodium soup from a specific store" or "commercially available tea" and display a link to purchase each.

[1343] Example prompt sentence:

[1344] Based on the data below, please suggest suitable foods for the user.

[1345] Weight: 65kg

[1346] Body temperature: 36.7°C

[1347] Health checkup results: High blood pressure

[1348] Emotional state: Stress

[1349] Please also include a purchase link for each of the suggested foods.

[1350] This allows users to easily find and purchase the foods that best suit their health and emotional state.

[1351] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1352] Step 1:

[1353] Users use smartphones or wearable devices to input their weight, body temperature, health check results, and emotional state. Emotional data is collected by the emotion engine by analyzing the user's facial expressions and voice in real time. The input data is stored on the device.

[1354] Inputs: weight, temperature, medical results, emotional state.

[1355] Output: Physical and emotional data stored on the device.

[1356] Step 2:

[1357] The device encrypts the stored physical and emotional data and transmits it to the server using the HTTPS protocol, ensuring the data is securely passed to the server.

[1358] Input: Physical and emotional data stored on the device.

[1359] Output: The encrypted data packet.

[1360] Step 3:

[1361] The server receives and decrypts the encrypted data sent from the terminal. The decrypted data undergoes a data integrity verification process and is then passed to the analysis engine.

[1362] Input: Encrypted data packet.

[1363] Output: Decoded physical and emotional data.

[1364] Step 4:

[1365] The server's analysis engine uses the received data to assess the user's health and emotional state. For example, it classifies the user's health status based on weight, body temperature, and medical checkup results, and classifies the user's emotional state, such as stress level, based on the emotional data.

[1366] Input: Decoded physical and emotional data.

[1367] Output: Health and emotional state assessment results.

[1368] Step 5:

[1369] The server searches a food database based on the user's assessed health and emotional state, including the nutritional content, characteristics, and product name of each food, and lists the foods that are best suited to the user's situation.

[1370] Input: Health and emotional state assessment results.

[1371] Output: The searched food list.

[1372] Step 6:

[1373] The server further adjusts the suggested food list, taking into account the user's preferences and allergy information, to generate a final recommended food list, which also includes a purchase link for each food item.

[1374] Input: Retrieved food list, user preferences and allergy information.

[1375] Output: A tailored food recommendation list.

[1376] Step 7:

[1377] The server encrypts the generated food recommendation list and sends it to the device, where data integrity is verified during the transmission process.

[1378] Input: Tailored recommended food list.

[1379] Output: Encrypted data packet.

[1380] Step 8:

[1381] The device receives the encrypted recommended food list and decrypts it. The decrypted list is displayed to the user through the application. The user can view the recommended food and click a purchase link to purchase the product on the online shopping site.

[1382] Input: Encrypted data packet.

[1383] Output: Decoded recommended food list.

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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).

[1391] 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.

[1392] 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."

[1393] 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.

[1394] 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).

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] 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.

[1405] The following is further disclosed regarding the above embodiment.

[1406] (Claim 1)

[1407] a means for a user to input physical data;

[1408] means for transmitting the input physiological data to a server;

[1409] a means for analyzing the received physiological data by the server and evaluating the health status of the user;

[1410] a means for searching a food database for specific foods suitable for the health condition based on the assessment;

[1411] a means of tailoring search results and generating specific food recommendations;

[1412] means for transmitting the generated recommended food list to a user's terminal;

[1413] A means for displaying the recommended food list received by the user's terminal

[1414] A system including:

[1415] (Claim 2)

[1416] 10. The system of claim 1, further comprising means for adjusting the recommended food list taking into account user preferences and allergy information.

[1417] (Claim 3)

[1418] 10. The system of claim 1, further comprising means for collecting physical data including physical examination results, weight, and temperature.

[1419] "Example 1"

[1420] (Claim 1)

[1421] a means for a user to input physical data;

[1422] means for transmitting the input physiological data to a server;

[1423] a means for analyzing the received physiological data by the server and evaluating the health status of the user;

[1424] a means for searching a food database for specific foods suitable for the health condition based on the assessment;

[1425] a means of tailoring search results and generating specific food recommendations;

[1426] means for transmitting the generated recommended food list to a user's terminal;

[1427] means for displaying the recommended food list received by the user's terminal;

[1428] means for adjusting the recommended food list to take into account the user's preferences and allergy information;

[1429] A system including:

[1430] (Claim 2)

[1431] 10. The system of claim 1, further comprising means for collecting physical data including physical examination results, weight, and temperature.

[1432] (Claim 3)

[1433] 2. The system according to claim 1, further comprising means for listing foods suitable for the user's health condition based on the results of searching the food database in the server.

[1434] "Application Example 1"

[1435] (Claim 1)

[1436] a means for a user to input physical data;

[1437] means for transmitting the input physiological data to a server;

[1438] a means for analyzing the received physiological data by the server and evaluating the health status of the user;

[1439] a means for searching a food database for specific foods suitable for the health condition based on the assessment;

[1440] a means of tailoring search results and generating specific food recommendations;

[1441] means for transmitting the generated recommended food list to a user's terminal;

[1442] means for displaying the recommended food list received by the user's terminal;

[1443] a means for providing a link for the user to directly purchase the recommended food products;

[1444] A means for the device displaying the recommended food list to function as a smartphone;

[1445] A system including:

[1446] (Claim 2)

[1447] 10. The system of claim 1, further comprising means for adjusting the recommended food list taking into account user preferences and allergy information.

[1448] (Claim 3)

[1449] 10. The system of claim 1, further comprising means for collecting physical data including physical examination results, weight, and temperature.

[1450] "Example 2: Combining Emotion Engines"

[1451] (Claim 1)

[1452] a means for a user to input physical and emotional data;

[1453] means for transmitting the input data to a server using a secure communication protocol;

[1454] means for verifying the integrity of the received physical data and emotional data;

[1455] means for analyzing the confirmed data with an analytical engine to assess the user's health and emotional state;

[1456] a means for searching a food database for specific foods appropriate for the health and emotional state based on the assessment;

[1457] a means for adjusting the search results to take into account the user's preferences and allergy information and generating a list of recommended foods;

[1458] A means for encrypting the generated recommended food list and transmitting it to a user's terminal;

[1459] A means for decoding and displaying the recommended food list received by the user's terminal.

[1460] A system including:

[1461] (Claim 2)

[1462] 10. The system of claim 1, further comprising means for recognizing the user's emotional state in real time using an emotion engine.

[1463] (Claim 3)

[1464] 10. The system of claim 1, further comprising means for collecting data including physical examination results, weight, temperature, and emotional state.

[1465] "Application example 2 when combining emotion engines"

[1466] (Claim 1)

[1467] a means for a user to input physical and emotional data;

[1468] means for transmitting the input physical data and emotion data to a server;

[1469] means for analyzing the received physical data and emotional data by the server to assess the health and emotional state of the user;

[1470] means for searching a food database for specific foods appropriate for the health and emotional state based on the assessment;

[1471] a means of tailoring search results and generating specific food recommendations;

[1472] means for transmitting the generated recommended food list to a user's terminal;

[1473] means for displaying the recommended food list received by the user's terminal;

[1474] A means to include links to purchase food items in the generated food recommendation list

[1475] A system including:

[1476] (Claim 2)

[1477] 10. The system of claim 1, further comprising means for adjusting the recommended food list taking into account user preferences and allergy information.

[1478] (Claim 3)

[1479] 10. The system of claim 1, further comprising means for collecting physical data including physical examination results, weight, and temperature. [Explanation of symbols]

[1480] 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 means for a user to input physical data; means for transmitting the input physiological data to a server; a means for analyzing the received physiological data by the server and evaluating the health status of the user; a means for searching a food database for specific foods suitable for the health condition based on the assessment; a means of tailoring search results and generating specific food recommendations; means for transmitting the generated recommended food list to a user's terminal; A means for displaying the recommended food list received by the user's terminal; A system including:

2. 10. The system of claim 1, further comprising means for adjusting the recommended food list to take into account user preferences and allergy information.

3. 10. The system of claim 1, further comprising means for collecting physical data including physical examination results, weight, and temperature.

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